Laurent Besacier

Also published as: L. Besacier


2020

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Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech
William Havard | Laurent Besacier | Jean-Pierre Chevrot
Proceedings of the 24th Conference on Computational Natural Language Learning

The language acquisition literature shows that children do not build their lexicon by segmenting the spoken input into phonemes and then building up words from them, but rather adopt a top-down approach and start by segmenting word-like units and then break them down into smaller units. This suggests that the ideal way of learning a language is by starting from full semantic units. In this paper, we investigate if this is also the case for a neural model of Visually Grounded Speech trained on a speech-image retrieval task. We evaluated how well such a network is able to learn a reliable speech-to-image mapping when provided with phone, syllable, or word boundary information. We present a simple way to introduce such information into an RNN-based model and investigate which type of boundary is the most efficient. We also explore at which level of the network’s architecture such information should be introduced so as to maximise its performances. Finally, we show that using multiple boundary types at once in a hierarchical structure, by which low-level segments are used to recompose high-level segments, is beneficial and yields better results than using low-level or high-level segments in isolation.

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Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)
Dorothee Beermann | Laurent Besacier | Sakriani Sakti | Claudia Soria
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

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Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
Marcely Zanon Boito | Aline Villavicencio | Laurent Besacier
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

For endangered languages, data collection campaigns have to accommodate the challenge that many of them are from oral tradition, and producing transcriptions is costly. Therefore, it is fundamental to translate them into a widely spoken language to ensure interpretability of the recordings. In this paper we investigate how the choice of translation language affects the posterior documentation work and potential automatic approaches which will work on top of the produced bilingual corpus. For answering this question, we use the MaSS multilingual speech corpus (Boito et al., 2020) for creating 56 bilingual pairs that we apply to the task of low-resource unsupervised word segmentation and alignment. Our results highlight that the choice of language for translation influences the word segmentation performance, and that different lexicons are learned by using different aligned translations. Lastly, this paper proposes a hybrid approach for bilingual word segmentation, combining boundary clues extracted from a non-parametric Bayesian model (Goldwater et al., 2009a) with the attentional word segmentation neural model from Godard et al. (2018). Our results suggest that incorporating these clues into the neural models’ input representation increases their translation and alignment quality, specially for challenging language pairs.

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FlauBERT: Unsupervised Language Model Pre-training for French
Hang Le | Loïc Vial | Jibril Frej | Vincent Segonne | Maximin Coavoux | Benjamin Lecouteux | Alexandre Allauzen | Benoit Crabbé | Laurent Besacier | Didier Schwab
Proceedings of the 12th Language Resources and Evaluation Conference

Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP.

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MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible
Marcely Zanon Boito | William Havard | Mahault Garnerin | Éric Le Ferrand | Laurent Besacier
Proceedings of the 12th Language Resources and Evaluation Conference

The CMU Wilderness Multilingual Speech Dataset (Black, 2019) is a newly published multilingual speech dataset based on recorded readings of the New Testament. It provides data to build Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) models for potentially 700 languages. However, the fact that the source content (the Bible) is the same for all the languages is not exploited to date.Therefore, this article proposes to add multilingual links between speech segments in different languages, and shares a large and clean dataset of 8,130 parallel spoken utterances across 8 languages (56 language pairs). We name this corpus MaSS (Multilingual corpus of Sentence-aligned Spoken utterances). The covered languages (Basque, English, Finnish, French, Hungarian, Romanian, Russian and Spanish) allow researches on speech-to-speech alignment as well as on translation for typologically different language pairs. The quality of the final corpus is attested by human evaluation performed on a corpus subset (100 utterances, 8 language pairs). Lastly, we showcase the usefulness of the final product on a bilingual speech retrieval task.

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Gender Representation in Open Source Speech Resources
Mahault Garnerin | Solange Rossato | Laurent Besacier
Proceedings of the 12th Language Resources and Evaluation Conference

With the rise of artificial intelligence (AI) and the growing use of deep-learning architectures, the question of ethics, transparency and fairness of AI systems has become a central concern within the research community. We address transparency and fairness in spoken language systems by proposing a study about gender representation in speech resources available through the Open Speech and Language Resource platform. We show that finding gender information in open source corpora is not straightforward and that gender balance depends on other corpus characteristics (elicited/non elicited speech, low/high resource language, speech task targeted). The paper ends with recommendations about metadata and gender information for researchers in order to assure better transparency of the speech systems built using such corpora.

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Représentation du genre dans des données open source de parole (Gender representation in open source speech resources 1 With the rise of artificial intelligence (AI) and the growing use of deep-learning architectures, the question of ethics and transparency in AI systems has become a central concern within the research community)
Mahault Garnerin | Solange Rossato | Laurent Besacier
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole

Avec l’essor de l’intelligence artificielle (IA) et l’utilisation croissante des architectures d’apprentissage profond, la question de l’éthique et de la transparence des systèmes d’IA est devenue une préoccupation centrale au sein de la communauté de recherche. Dans cet article, nous proposons une étude sur la représentation du genre dans les ressources de parole disponibles sur la plateforme Open Speech and Language Resource. Un tout premier résultat est la difficulté d’accès aux informations sur le genre des locuteurs. Ensuite, nous montrons que l’équilibre entre les catégories de genre dépend de diverses caractéristiques des corpus (discours élicité ou non, tâche adressée). En nous appuyant sur des travaux antérieurs, nous reprenons quelques principes concernant les métadonnées dans l’optique d’assurer une meilleure transparence des systèmes de parole construits à l’aide de ces corpus.

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FlauBERT : des modèles de langue contextualisés pré-entraînés pour le français (FlauBERT : Unsupervised Language Model Pre-training for French)
Hang Le | Loïc Vial | Jibril Frej | Vincent Segonne | Maximin Coavoux | Benjamin Lecouteux | Alexandre Allauzen | Benoît Crabbé | Laurent Besacier | Didier Schwab
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 2 : Traitement Automatique des Langues Naturelles

Les modèles de langue pré-entraînés sont désormais indispensables pour obtenir des résultats à l’état-de-l’art dans de nombreuses tâches du TALN. Tirant avantage de l’énorme quantité de textes bruts disponibles, ils permettent d’extraire des représentations continues des mots, contextualisées au niveau de la phrase. L’efficacité de ces représentations pour résoudre plusieurs tâches de TALN a été démontrée récemment pour l’anglais. Dans cet article, nous présentons et partageons FlauBERT, un ensemble de modèles appris sur un corpus français hétérogène et de taille importante. Des modèles de complexité différente sont entraînés à l’aide du nouveau supercalculateur Jean Zay du CNRS. Nous évaluons nos modèles de langue sur diverses tâches en français (classification de textes, paraphrase, inférence en langage naturel, analyse syntaxique, désambiguïsation automatique) et montrons qu’ils surpassent souvent les autres approches sur le référentiel d’évaluation FLUE également présenté ici.

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Pratiques d’évaluation en ASR et biais de performance (Evaluation methodology in ASR and performance bias)
Mahault Garnerin | Solange Rossato | Laurent Besacier
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). 2e atelier Éthique et TRaitemeNt Automatique des Langues (ETeRNAL)

Nous proposons une réflexion sur les pratiques d’évaluation des systèmes de reconnaissance automatique de la parole (ASR). Après avoir défini la notion de discrimination d’un point de vue légal et la notion d’équité dans les systèmes d’intelligence artificielle, nous nous intéressons aux pratiques actuelles lors des grandes campagnes d’évaluation. Nous observons que la variabilité de la parole et plus particulièrement celle de l’individu n’est pas prise en compte dans les protocoles d’évaluation actuels rendant impossible l’étude de biais potentiels dans les systèmes.

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ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020
Maha Elbayad | Ha Nguyen | Fethi Bougares | Natalia Tomashenko | Antoine Caubrière | Benjamin Lecouteux | Yannick Estève | Laurent Besacier
Proceedings of the 17th International Conference on Spoken Language Translation

This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and simultaneous speech translation. ON-TRAC Consortium is composed of researchers from three French academic laboratories: LIA (Avignon Université), LIG (Université Grenoble Alpes), and LIUM (Le Mans Université). Attention-based encoder-decoder models, trained end-to-end, were used for our submissions to the offline speech translation track. Our contributions focused on data augmentation and ensembling of multiple models. In the simultaneous speech translation track, we build on Transformer-based wait-k models for the text-to-text subtask. For speech-to-text simultaneous translation, we attach a wait-k MT system to a hybrid ASR system. We propose an algorithm to control the latency of the ASR+MT cascade and achieve a good latency-quality trade-off on both subtasks.

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Enabling Interactive Transcription in an Indigenous Community
Eric Le Ferrand | Steven Bird | Laurent Besacier
Proceedings of the 28th International Conference on Computational Linguistics

We propose a novel transcription workflow which combines spoken term detection and human-in-the-loop, together with a pilot experiment. This work is grounded in an almost zero-resource scenario where only a few terms have so far been identified, involving two endangered languages. We show that in the early stages of transcription, when the available data is insufficient to train a robust ASR system, it is possible to take advantage of the transcription of a small number of isolated words in order to bootstrap the transcription of a speech collection.

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Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation
Hang Le | Juan Pino | Changhan Wang | Jiatao Gu | Didier Schwab | Laurent Besacier
Proceedings of the 28th International Conference on Computational Linguistics

We introduce dual-decoder Transformer, a new model architecture that jointly performs automatic speech recognition (ASR) and multilingual speech translation (ST). Our models are based on the original Transformer architecture (Vaswani et al., 2017) but consist of two decoders, each responsible for one task (ASR or ST). Our major contribution lies in how these decoders interact with each other: one decoder can attend to different information sources from the other via a dual-attention mechanism. We propose two variants of these architectures corresponding to two different levels of dependencies between the decoders, called the parallel and cross dual-decoder Transformers, respectively. Extensive experiments on the MuST-C dataset show that our models outperform the previously-reported highest translation performance in the multilingual settings, and outperform as well bilingual one-to-one results. Furthermore, our parallel models demonstrate no trade-off between ASR and ST compared to the vanilla multi-task architecture. Our code and pre-trained models are available at https://github.com/formiel/speech-translation.

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Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English
Maha Elbayad | Michael Ustaszewski | Emmanuelle Esperança-Rodier | Francis Brunet-Manquat | Jakob Verbeek | Laurent Besacier
Proceedings of the 28th International Conference on Computational Linguistics

We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al. 2018) and attention-based Transformer (Vaswani et al. 2017) are considered. We investigate, for both architectures, the impact of online decoding constraints on the translation quality through a carefully designed human evaluation on English-German and German-English language pairs, the latter being particularly sensitive to latency constraints. The evaluation results allow us to identify the strengths and shortcomings of each model when we shift to the online setup.

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Monolingual Adapters for Zero-Shot Neural Machine Translation
Jerin Philip | Alexandre Berard | Matthias Gallé | Laurent Besacier
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose a novel adapter layer formalism for adapting multilingual models. They are more parameter-efficient than existing adapter layers while obtaining as good or better performance. The layers are specific to one language (as opposed to bilingual adapters) allowing to compose them and generalize to unseen language-pairs. In this zero-shot setting, they obtain a median improvement of +2.77 BLEU points over a strong 20-language multilingual Transformer baseline trained on TED talks.

2019

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Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 2019
Fahimeh Saleh | Alexandre Berard | Ioan Calapodescu | Laurent Besacier
Proceedings of the 3rd Workshop on Neural Generation and Translation

Recently, neural models led to significant improvements in both machine translation (MT) and natural language generation tasks (NLG). However, generation of long descriptive summaries conditioned on structured data remains an open challenge. Likewise, MT that goes beyond sentence-level context is still an open issue (e.g., document-level MT or MT with metadata). To address these challenges, we propose to leverage data from both tasks and do transfer learning between MT, NLG, and MT with source-side metadata (MT+NLG). First, we train document-based MT systems with large amounts of parallel data. Then, we adapt these models to pure NLG and MT+NLG tasks by fine-tuning with smaller amounts of domain-specific data. This end-to-end NLG approach, without data selection and planning, outperforms the previous state of the art on the Rotowire NLG task. We participated to the “Document Generation and Translation” task at WNGT 2019, and ranked first in all tracks.

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Word Recognition, Competition, and Activation in a Model of Visually Grounded Speech
William N. Havard | Jean-Pierre Chevrot | Laurent Besacier
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

In this paper, we study how word-like units are represented and activated in a recurrent neural model of visually grounded speech. The model used in our experiments is trained to project an image and its spoken description in a common representation space. We show that a recurrent model trained on spoken sentences implicitly segments its input into word-like units and reliably maps them to their correct visual referents. We introduce a methodology originating from linguistics to analyse the representation learned by neural networks – the gating paradigm – and show that the correct representation of a word is only activated if the network has access to first phoneme of the target word, suggesting that the network does not rely on a global acoustic pattern. Furthermore, we find out that not all speech frames (MFCC vectors in our case) play an equal role in the final encoded representation of a given word, but that some frames have a crucial effect on it. Finally we suggest that word representation could be activated through a process of lexical competition.

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Motivations, challenges, and perspectives for the development of an Automatic Speech Recognition System for the under-resourced Ngiemboon Language
Patrice Yemmene | Laurent Besacier
Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers

2018

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Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation
Ali Can Kocabiyikoglu | Laurent Besacier | Olivier Kraif
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments
Pierre Godard | Gilles Adda | Martine Adda-Decker | Juan Benjumea | Laurent Besacier | Jamison Cooper-Leavitt | Guy-Noel Kouarata | Lori Lamel | Hélène Maynard | Markus Mueller | Annie Rialland | Sebastian Stueker | François Yvon | Marcely Zanon-Boito
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville)
Annie Rialland | Martine Adda-Decker | Guy-Noël Kouarata | Gilles Adda | Laurent Besacier | Lori Lamel | Elodie Gauthier | Pierre Godard | Jamison Cooper-Leavitt
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction
Maha Elbayad | Laurent Besacier | Jakob Verbeek
Proceedings of the 22nd Conference on Computational Natural Language Learning

Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on the input encoding. Both are interfaced with an attention mechanism that recombines a fixed encoding of the source tokens based on the decoder state. We propose an alternative approach which instead relies on a single 2D convolutional neural network across both sequences. Each layer of our network re-codes source tokens on the basis of the output sequence produced so far. Attention-like properties are therefore pervasive throughout the network. Our model yields excellent results, outperforming state-of-the-art encoder-decoder systems, while being conceptually simpler and having fewer parameters.

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Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation
Xuanli He | Quan Tran | William Havard | Laurent Besacier | Ingrid Zukerman | Gholamreza Haffari
Proceedings of the Australasian Language Technology Association Workshop 2018

In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i.e. human transcriptions, instead of Automatic Speech Recognition (ASR)’s transcriptions. In spoken dialog systems, however, the agent would only have access to noisy ASR transcriptions, which may further suffer performance degradation due to domain shift. In this paper, we explore the effectiveness of using both acoustic and textual signals, either oracle or ASR transcriptions, and investigate speaker domain adaptation for DA classification. Our multimodal model proves to be superior to the unimodal models, particularly when the oracle transcriptions are not available. We also propose an effective method for speaker domain adaptation, which achieves competitive results.

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Token-level and sequence-level loss smoothing for RNN language models
Maha Elbayad | Laurent Besacier | Jakob Verbeek
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Despite the effectiveness of recurrent neural network language models, their maximum likelihood estimation suffers from two limitations. It treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. Second, it suffers from ’exposure bias’: during training tokens are predicted given ground-truth sequences, while at test time prediction is conditioned on generated output sequences. To overcome these limitations we build upon the recent reward augmented maximum likelihood approach that encourages the model to predict sentences that are close to the ground truth according to a given performance metric. We extend this approach to token-level loss smoothing, and propose improvements to the sequence-level smoothing approach. Our experiments on two different tasks, image captioning and machine translation, show that token-level and sequence-level loss smoothing are complementary, and significantly improve results.

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Analyzing Learned Representations of a Deep ASR Performance Prediction Model
Zied Elloumi | Laurent Besacier | Olivier Galibert | Benjamin Lecouteux
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

This paper addresses a relatively new task: prediction of ASR performance on unseen broadcast programs. In a previous paper, we presented an ASR performance prediction system using CNNs that encode both text (ASR transcript) and speech, in order to predict word error rate. This work is dedicated to the analysis of speech signal embeddings and text embeddings learnt by the CNN while training our prediction model. We try to better understand which information is captured by the deep model and its relation with different conditioning factors. It is shown that hidden layers convey a clear signal about speech style, accent and broadcast type. We then try to leverage these 3 types of information at training time through multi-task learning. Our experiments show that this allows to train slightly more efficient ASR performance prediction systems that - in addition - simultaneously tag the analyzed utterances according to their speech style, accent and broadcast program origin.

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Adaptor Grammars for the Linguist: Word Segmentation Experiments for Very Low-Resource Languages
Pierre Godard | Laurent Besacier | François Yvon | Martine Adda-Decker | Gilles Adda | Hélène Maynard | Annie Rialland
Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

Computational Language Documentation attempts to make the most recent research in speech and language technologies available to linguists working on language preservation and documentation. In this paper, we pursue two main goals along these lines. The first is to improve upon a strong baseline for the unsupervised word discovery task on two very low-resource Bantu languages, taking advantage of the expertise of linguists on these particular languages. The second consists in exploring the Adaptor Grammar framework as a decision and prediction tool for linguists studying a new language. We experiment 162 grammar configurations for each language and show that using Adaptor Grammars for word segmentation enables us to test hypotheses about a language. Specializing a generic grammar with language specific knowledge leads to great improvements for the word discovery task, ultimately achieving a leap of about 30% token F-score from the results of a strong baseline.

2017

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Traitement des Mots Hors Vocabulaire pour la Traduction Automatique de Document OCRisés en Arabe (This article presents a new system that automatically translates images of arabic documents)
Kamel Bouzidi | Zied Elloumi | Laurent Besacier | Benjamin Lecouteux | Mohamed-Faouzi Benzeghiba
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs

Cet article présente un système original de traduction de documents numérisés en arabe. Deux modules sont cascadés : un système de reconnaissance optique de caractères (OCR) en arabe et un système de traduction automatique (TA) arabe-français. Le couplage OCR-TA a été peu abordé dans la littérature et l’originalité de cette étude consiste à proposer un couplage étroit entre OCR et TA ainsi qu’un traitement spécifique des mots hors vocabulaire (MHV) engendrés par les erreurs d’OCRisation. Le couplage OCR-TA par treillis et notre traitement des MHV par remplacement selon une mesure composite qui prend en compte forme de surface et contexte du mot, permettent une amélioration significative des performances de traduction. Les expérimentations sont réalisés sur un corpus de journaux numérisés en arabe et permettent d’obtenir des améliorations en score BLEU de 3,73 et 5,5 sur les corpus de développement et de test respectivement.

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Using Word Embedding for Cross-Language Plagiarism Detection
Jérémy Ferrero | Laurent Besacier | Didier Schwab | Frédéric Agnès
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

This paper proposes to use distributed representation of words (word embeddings) in cross-language textual similarity detection. The main contributions of this paper are the following: (a) we introduce new cross-language similarity detection methods based on distributed representation of words; (b) we combine the different methods proposed to verify their complementarity and finally obtain an overall F1 score of 89.15% for English-French similarity detection at chunk level (88.5% at sentence level) on a very challenging corpus.

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CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity
Jérémy Ferrero | Laurent Besacier | Didier Schwab | Frédéric Agnès
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We present our submitted systems for Semantic Textual Similarity (STS) Track 4 at SemEval-2017. Given a pair of Spanish-English sentences, each system must estimate their semantic similarity by a score between 0 and 5. In our submission, we use syntax-based, dictionary-based, context-based, and MT-based methods. We also combine these methods in unsupervised and supervised way. Our best run ranked 1st on track 4a with a correlation of 83.02% with human annotations.

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Deep Investigation of Cross-Language Plagiarism Detection Methods
Jérémy Ferrero | Laurent Besacier | Didier Schwab | Frédéric Agnès
Proceedings of the 10th Workshop on Building and Using Comparable Corpora

This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts). We investigate cross-language plagiarism detection methods for 6 language pairs on 2 granularities of text units in order to draw robust conclusions on the best methods while deeply analyzing correlations across document styles and languages.

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Amharic-English Speech Translation in Tourism Domain
Michael Melese | Laurent Besacier | Million Meshesha
Proceedings of the Workshop on Speech-Centric Natural Language Processing

This paper describes speech translation from Amharic-to-English, particularly Automatic Speech Recognition (ASR) with post-editing feature and Amharic-English Statistical Machine Translation (SMT). ASR experiment is conducted using morpheme language model (LM) and phoneme acoustic model(AM). Likewise,SMT conducted using word and morpheme as unit. Morpheme based translation shows a 6.29 BLEU score at a 76.4% of recognition accuracy while word based translation shows a 12.83 BLEU score using 77.4% word recognition accuracy. Further, after post-edit on Amharic ASR using corpus based n-gram, the word recognition accuracy increased by 1.42%. Since post-edit approach reduces error propagation, the word based translation accuracy improved by 0.25 (1.95%) BLEU score. We are now working towards further improving propagated errors through different algorithms at each unit of speech translation cascading component.

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LIG-CRIStAL Submission for the WMT 2017 Automatic Post-Editing Task
Alexandre Bérard | Laurent Besacier | Olivier Pietquin
Proceedings of the Second Conference on Machine Translation

2016

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The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents
Johann Poignant | Mateusz Budnik | Hervé Bredin | Claude Barras | Mickael Stefas | Pierrick Bruneau | Gilles Adda | Laurent Besacier | Hazim Ekenel | Gil Francopoulo | Javier Hernando | Joseph Mariani | Ramon Morros | Georges Quénot | Sophie Rosset | Thomas Tamisier
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we describe the organization and the implementation of the CAMOMILE collaborative annotation framework for multimodal, multimedia, multilingual (3M) data. Given the versatile nature of the analysis which can be performed on 3M data, the structure of the server was kept intentionally simple in order to preserve its genericity, relying on standard Web technologies. Layers of annotations, defined as data associated to a media fragment from the corpus, are stored in a database and can be managed through standard interfaces with authentication. Interfaces tailored specifically to the needed task can then be developed in an agile way, relying on simple but reliable services for the management of the centralized annotations. We then present our implementation of an active learning scenario for person annotation in video, relying on the CAMOMILE server; during a dry run experiment, the manual annotation of 716 speech segments was thus propagated to 3504 labeled tracks. The code of the CAMOMILE framework is distributed in open source.

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Collecting Resources in Sub-Saharan African Languages for Automatic Speech Recognition: a Case Study of Wolof
Elodie Gauthier | Laurent Besacier | Sylvie Voisin | Michael Melese | Uriel Pascal Elingui
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This article presents the data collected and ASR systems developped for 4 sub-saharan african languages (Swahili, Hausa, Amharic and Wolof). To illustrate our methodology, the focus is made on Wolof (a very under-resourced language) for which we designed the first ASR system ever built in this language. All data and scripts are available online on our github repository.

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A Multilingual, Multi-style and Multi-granularity Dataset for Cross-language Textual Similarity Detection
Jérémy Ferrero | Frédéric Agnès | Laurent Besacier | Didier Schwab
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper we describe our effort to create a dataset for the evaluation of cross-language textual similarity detection. We present preexisting corpora and their limits and we explain the various gathered resources to overcome these limits and build our enriched dataset. The proposed dataset is multilingual, includes cross-language alignment for different granularities (from chunk to document), is based on both parallel and comparable corpora and contains human and machine translated texts. Moreover, it includes texts written by multiple types of authors (from average to professionals). With the obtained dataset, we conduct a systematic and rigorous evaluation of several state-of-the-art cross-language textual similarity detection methods. The evaluation results are reviewed and discussed. Finally, dataset and scripts are made publicly available on GitHub: http://github.com/FerreroJeremy/Cross-Language-Dataset.

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MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP
Alexandre Bérard | Christophe Servan | Olivier Pietquin | Laurent Besacier
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words). MultiVec includes word2vec’s features, paragraph vector (batch and online) and bivec for bilingual distributed representations. MultiVec also includes different distance measures between words and sequences of words. The toolkit is written in C++ and is aimed at being fast (in the same order of magnitude as word2vec), easy to use, and easy to extend. It has been evaluated on several NLP tasks: the analogical reasoning task, sentiment analysis, and crosslingual document classification.

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Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks
Othman Zennaki | Nasredine Semmar | Laurent Besacier
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This work focuses on the development of linguistic analysis tools for resource-poor languages. We use a parallel corpus to produce a multilingual word representation based only on sentence level alignment. This representation is combined with the annotated source side (resource-rich language) of the parallel corpus to train text analysis tools for resource-poor languages. Our approach is based on Recurrent Neural Networks (RNN) and has the following advantages: (a) it does not use word alignment information, (b) it does not assume any knowledge about foreign languages, which makes it applicable to a wide range of resource-poor languages, (c) it provides truly multilingual taggers. In a previous study, we proposed a method based on Simple RNN to automatically induce a Part-Of-Speech (POS) tagger. In this paper, we propose an improvement of our neural model. We investigate the Bidirectional RNN and the inclusion of external information (for instance low level information from Part-Of-Speech tags) in the RNN to train a more complex tagger (for instance, a multilingual super sense tagger). We demonstrate the validity and genericity of our method by using parallel corpora (obtained by manual or automatic translation). Our experiments are conducted to induce cross-lingual POS and super sense taggers.

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Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?
Christophe Servan | Alexandre Bérard | Zied Elloumi | Hervé Blanchon | Laurent Besacier
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper presents an approach combining lexico-semantic resources and distributed representations of words applied to the evaluation in machine translation (MT). This study is made through the enrichment of a well-known MT evaluation metric: METEOR. METEOR enables an approximate match (synonymy or morphological similarity) between an automatic and a reference translation. Our experiments are made in the framework of the Metrics task of WMT 2014. We show that distributed representations are a good alternative to lexico-semanticresources for MT evaluation and they can even bring interesting additional information. The augmented versions of METEOR, using vector representations, are made available on our Github page.

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Projection Interlingue d’Étiquettes pour l’Annotation Sémantique Non Supervisée (Cross-lingual Annotation Projection for Unsupervised Semantic Tagging)
Othman Zennaki | Nasredine Semmar | Laurent Besacier
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)

Nos travaux portent sur la construction rapide d’outils d’analyse linguistique pour des langues peu dotées en ressources. Dans une précédente contribution, nous avons proposé une méthode pour la construction automatique d’un analyseur morpho-syntaxique via une projection interlingue d’annotations linguistiques à partir de corpus parallèles (méthode fondée sur les réseaux de neurones récurrents). Nous présentons, dans cet article, une amélioration de notre modèle neuronal, avec la prise en compte d’informations linguistiques externes pour un annotateur plus complexe. En particulier, nous proposons d’intégrer des annotations morpho-syntaxiques dans notre architecture neuronale pour l’apprentissage non supervisé d’annotateurs sémantiques multilingues à gros grain (annotation en SuperSenses). Nous montrons la validité de notre méthode et sa généricité sur l’italien et le français et étudions aussi l’impact de la qualité du corpus parallèle sur notre approche (généré par traduction manuelle ou automatique). Nos expériences portent sur la projection d’annotations de l’anglais vers le français et l’italien.

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Word2Vec vs DBnary ou comment (ré)concilier représentations distribuées et réseaux lexico-sémantiques ? Le cas de l’évaluation en traduction automatique (Word2Vec vs DBnary or how to bring back together vector representations and lexical resources ? A case study for machine translation evaluation)
Christophe Servan | Zied Elloumi | Hervé Blanchon | Laurent Besacier
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)

Cet article présente une approche associant réseaux lexico-sémantiques et représentations distribuées de mots appliquée à l’évaluation de la traduction automatique. Cette étude est faite à travers l’enrichissement d’une métrique bien connue pour évaluer la traduction automatique (TA) : METEOR. METEOR permet un appariement approché (similarité morphologique ou synonymie) entre une sortie de système automatique et une traduction de référence. Nos expérimentations s’appuient sur la tâche Metrics de la campagne d’évaluation WMT 2014 et montrent que les représentations distribuées restent moins performantes que les ressources lexico-sémantiques pour l’évaluation en TA mais peuvent néammoins apporter un complément d’information intéressant à ces dernières.

2015

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Utilisation de mesures de confiance pour améliorer le décodage en traduction de parole
Laurent Besacier | Benjamin Lecouteux | Luong Ngoc Quang
Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Les mesures de confiance au niveau mot (Word Confidence Estimation - WCE) pour la traduction auto- matique (TA) ou pour la reconnaissance automatique de la parole (RAP) attribuent un score de confiance à chaque mot dans une hypothèse de transcription ou de traduction. Dans le passé, l’estimation de ces mesures a le plus souvent été traitée séparément dans des contextes RAP ou TA. Nous proposons ici une estimation conjointe de la confiance associée à un mot dans une hypothèse de traduction automatique de la parole (TAP). Cette estimation fait appel à des paramètres issus aussi bien des systèmes de transcription de la parole (RAP) que des systèmes de traduction automatique (TA). En plus de la construction de ces estimateurs de confiance robustes pour la TAP, nous utilisons les informations de confiance pour re-décoder nos graphes d’hypothèses de traduction. Les expérimentations réalisées montrent que l’utilisation de ces mesures de confiance au cours d’une seconde passe de décodage permettent d’obtenir une amélioration significative des performances de traduction (évaluées avec la métrique BLEU - gains de deux points par rapport à notre système de traduc- tion de parole de référence). Ces expériences sont faites pour une tâche de TAP (français-anglais) pour laquelle un corpus a été spécialement conçu (ce corpus, mis à la disposition de la communauté TALN, est aussi décrit en détail dans l’article).

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Utilisation des réseaux de neurones récurrents pour la projection interlingue d’étiquettes morpho-syntaxiques à partir d’un corpus parallèle
Othman Zennaki | Nasredine Semmar | Laurent Besacier
Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

La construction d’outils d’analyse linguistique pour les langues faiblement dotées est limitée, entre autres, par le manque de corpus annotés. Dans cet article, nous proposons une méthode pour construire automatiquement des outils d’analyse via une projection interlingue d’annotations linguistiques en utilisant des corpus parallèles. Notre approche n’utilise pas d’autres sources d’information, ce qui la rend applicable à un large éventail de langues peu dotées. Nous proposons d’utiliser les réseaux de neurones récurrents pour projeter les annotations d’une langue à une autre (sans utiliser d’information d’alignement des mots). Dans un premier temps, nous explorons la tâche d’annotation morpho-syntaxique. Notre méthode combinée avec une méthode de projection d’annotation basique (utilisant l’alignement mot à mot), donne des résultats comparables à ceux de l’état de l’art sur une tâche similaire.

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Automated Translation of a Literary Work: A Pilot Study
Laurent Besacier | Lane Schwartz
Proceedings of the Fourth Workshop on Computational Linguistics for Literature

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Unsupervised and Lightly Supervised Part-of-Speech Tagging Using Recurrent Neural Networks
Othman Zennaki | Nasredine Semmar | Laurent Besacier
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

2014

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Word Confidence Estimation for SMT N-best List Re-ranking
Ngoc-Quang Luong | Laurent Besacier | Benjamin Lecouteux
Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation

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LIG System for Word Level QE task at WMT14
Ngoc-Quang Luong | Laurent Besacier | Benjamin Lecouteux
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Machine translation for litterature: a pilot study (Traduction automatisée d’une oeuvre littéraire: une étude pilote) [in French]
Laurent Besacier
Proceedings of TALN 2014 (Volume 2: Short Papers)

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An efficient two-pass decoder for SMT using word confidence estimation
Ngoc-Quang Luong | Laurent Besacier | Benjamin Lecouteux
Proceedings of the 17th Annual conference of the European Association for Machine Translation

2013

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LIG System for WMT13 QE Task: Investigating the Usefulness of Features in Word Confidence Estimation for MT
Ngoc-Quang Luong | Benjamin Lecouteux | Laurent Besacier
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Fast Bootstrapping of Grapheme to Phoneme System for Under-resourced Languages - Application to the Iban Language
Sarah Samson Juan | Laurent Besacier
Proceedings of the 4th Workshop on South and Southeast Asian Natural Language Processing

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Urdu Hindi Machine Transliteration using SMT
M. G. Abbas Malik | Christian Boitet | Laurent Besacier | Pushpak Bhattacharyya
Proceedings of the 4th Workshop on South and Southeast Asian Natural Language Processing

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Discriminative statistical approaches for multilingual speech understanding (Approches statistiques discriminantes pour l’interprétation sémantique multilingue de la parole) [in French]
Bassam Jabaian | Fabrice Lefèvre | Laurent Besacier
Proceedings of TALN 2013 (Volume 1: Long Papers)

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Driven Decoding for machine translation (Vers un décodage guidé pour la traduction automatique) [in French]
Benjamin Lecouteux | Laurent Besacier
Proceedings of TALN 2013 (Volume 2: Short Papers)

2012

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Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP
Laurent Besacier | Benjamin Lecouteux | Gilles Sérasset
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP

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Développement de ressources en swahili pour un sytème de reconnaisance automatique de la parole (Developments of Swahili resources for an automatic speech recognition system) [in French]
Hadrien Gelas | Laurent Besacier | François Pellegrino
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP

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Robustesse et portabilités multilingue et multi-domaines des systèmes de compréhension de la parole : les corpus du projet PortMedia (Robustness and portability of spoken language understanding systems among languages and domains : the PORTMEDIA project) [in French]
Fabrice Lefèvre | Djamel Mostefa | Laurent Besacier | Yannick Estève | Matthieu Quignard | Nathalie Camelin | Benoit Favre | Bassam Jabaian | Lina Rojas-Barahona
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP

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Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 4: Invited Conferences
Laurent Besacier | Hervé Blanchon | Marie-Paule Jacques | Nathalie Vallée | Gilles Sérasset
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 4: Invited Conferences

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Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 5: Software Demonstrations
Laurent Besacier | Hervé Blanchon | Gilles Sérasset
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 5: Software Demonstrations

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Collection of a Large Database of French-English SMT Output Corrections
Marion Potet | Emmanuelle Esperança-Rodier | Laurent Besacier | Hervé Blanchon
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Corpus-based approaches to machine translation (MT) rely on the availability of parallel corpora. To produce user-acceptable translation outputs, such systems need high quality data to be efficiency trained, optimized and evaluated. However, building high quality dataset is a relatively expensive task. In this paper, we describe the data collection and analysis of a large database of 10.881 SMT translation output hypotheses manually corrected. These post-editions were collected using Amazon's Mechanical Turk, following some ethical guidelines. A complete analysis of the collected data pointed out a high quality of the corrections with more than 87 % of the collected post-editions that improve hypotheses and more than 94 % of the crowdsourced post-editions which are at least of professional quality. We also post-edited 1,500 gold-standard reference translations (of bilingual parallel corpora generated by professional) and noticed that 72 % of these translations needed to be corrected during post-edition. We computed a proximity measure between the differents kind of translations and pointed out that reference translations are as far from the hypotheses than from the corrected hypotheses (i.e. the post-editions). In light of these last findings, we discuss the adequation of text-based generated reference translations to train setence-to-sentence based SMT systems.

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Leveraging study of robustness and portability of spoken language understanding systems across languages and domains: the PORTMEDIA corpora
Fabrice Lefèvre | Djamel Mostefa | Laurent Besacier | Yannick Estève | Matthieu Quignard | Nathalie Camelin | Benoit Favre | Bassam Jabaian | Lina M. Rojas-Barahona
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The PORTMEDIA project is intended to develop new corpora for the evaluation of spoken language understanding systems. The newly collected data are in the field of human-machine dialogue systems for tourist information in French in line with the MEDIA corpus. Transcriptions and semantic annotations, obtained by low-cost procedures, are provided to allow a thorough evaluation of the systems' capabilities in terms of robustness and portability across languages and domains. A new test set with some adaptation data is prepared for each case: in Italian as an example of a new language, for ticket reservation as an example of a new domain. Finally the work is complemented by the proposition of a new high level semantic annotation scheme well-suited to dialogue data.

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Analyse des performances de modèles de langage sub-lexicale pour des langues peu-dotées à morphologie riche (Performance analysis of sub-word language modeling for under-resourced languages with rich morphology: case study on Swahili and Amharic) [in French]
Hadrien Gelas | Solomon Teferra Abate | Laurent Besacier | François Pellegrino
JEP-TALN-RECITAL 2012, Workshop TALAf 2012: Traitement Automatique des Langues Africaines (TALAf 2012: African Language Processing)

2011

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The LIGA (LIG/LIA) Machine Translation System for WMT 2011
Marion Potet | Raphaël Rubino | Benjamin Lecouteux | Stéphane Huet | Laurent Besacier | Hervé Blanchon | Fabrice Lefèvre
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Oracle-based Training for Phrase-based Statistical Machine Translation
Marion Potet | Emmanuelle Esperança-Rodier | Hervé Blanchon | Laurent Besacier
Proceedings of the 15th Annual conference of the European Association for Machine Translation

2010

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A fully unsupervised approach for mining parallel data from comparable corpora
Thi Ngoc Diep Do | Laurent Besacier | Eric Castelli
Proceedings of the 14th Annual conference of the European Association for Machine Translation

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The LIG Machine Translation System for WMT 2010
Marion Potet | Laurent Besacier | Hervé Blanchon
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Boosting N-gram Coverage for Unsegmented Languages Using Multiple Text Segmentation Approach
Solomon Teferra Abate | Laurent Besacier | Sopheap Seng
Proceedings of the 1st Workshop on South and Southeast Asian Natural Language Processing

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Automatic Identification of Arabic Dialects
Mohamed Belgacem | Georges Antoniadis | Laurent Besacier
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In this work, automatic recognition of Arabic dialects is proposed. An acoustic survey of the proportion of vocalic intervals and the standard deviation of consonantal intervals in nine dialects (Tunisia, Morocco, Algeria, Egypt, Syria, Lebanon, Yemen, Golf’s Countries and Iraq) is performed using the platform Alize and Gaussian Mixture Models (GMM). The results show the complexity of the automatic identification of Arabic dialects since. No clear border can be found between the dialects, but a gradual transition between them. They can even vary slightly from one city to another. The existence of this gradual change is easy to understand: it corresponds to a human and social reality, to the contact, friendships forged and affinity in the environment more or less immediate of the individual. This document also raises questions about the classes or macro classes of Arabic dialects noticed from the confusion matrix and the design of the hierarchical tree obtained.

2009

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Mining a Comparable Text Corpus for a Vietnamese-French Statistical Machine Translation System
Thi-Ngoc-Diep Do | Viet-Bac Le | Brigitte Bigi | Laurent Besacier | Eric Castelli
Proceedings of the Fourth Workshop on Statistical Machine Translation

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A Hybrid Model for Urdu Hindi Transliteration
Abbas Malik | Laurent Besacier | Christian Boitet | Pushpak Bhattacharyya
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

2008

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First Broadcast News Transcription System for Khmer Language
Sopheap Seng | Sethserey Sam | Laurent Besacier | Brigitte Bigi | Eric Castelli
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper we present an overview on the development of a large vocabulary continuous speech recognition (LVCSR) system for Khmer, the official language of Cambodia, spoken by more than 15 million people. As an under-resourced language, develop a LVCSR system for Khmer is a challenging task. We describe our methodologies for quick language data collection and processing for language modeling and acoustic modeling. For language modeling, we investigate the use of word and sub-word as basic modeling unit in order to see the potential of sub-word units in the case of unsegmented language like Khmer. Grapheme-based acoustic modeling is used to quickly build our Khmer language acoustic model. Furthermore, the approaches and tools used for the development of our system are documented and made publicly available on the web. We hope this will contribute to accelerate the development of LVCSR system for a new language, especially for under-resource languages of developing countries where resources and expertise are limited.

2006

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A French Non-Native Corpus for Automatic Speech Recognition
Tien-Ping Tan | Laurent Besacier
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Automatic speech recognition (ASR) technology has achieved a level of maturity, where it is already practical to be used by novice users. However, most non-native speakers are still not comfortable with services including ASR systems, because of the accuracy on non-native speakers. This paper describes our approach in constructing a non-native corpus particularly in French for testing and adapting non-native speaker for automatic speech recognition. Finally, we also propose in this paper a method for detecting pronunciation variants and possible pronunciation mistakes by non-native speakers.

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IBM MASTOR SYSTEM: Multilingual Automatic Speech-to-Speech Translator
Yuqing Gao | Bowen Zhou | Ruhi Sarikaya | Mohamed Afify | Hong-Kwang Kuo | Wei-zhong Zhu | Yonggang Deng | Charles Prosser | Wei Zhang | Laurent Besacier
Proceedings of the First International Workshop on Medical Speech Translation

2004

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Spoken and Written Language Resources for Vietnamese
Viet-Bac Le | Do-Dat Tran | Eric Castelli | Laurent Besacier | Jean-François Serignat
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2000

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A New Methodology for Speech Corpora Definition from Internet Documents
D. Vaufreydaz | C. Bergamini | J.F. Serignat | L. Besacier | M. Akbar
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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