Haithem Afli


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Hierarchical Deep Learning for Arabic Dialect Identification
Gael de Francony | Victor Guichard | Praveen Joshi | Haithem Afli | Abdessalam Bouchekif
Proceedings of the Fourth Arabic Natural Language Processing Workshop

In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification. The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification. The main idea is to separate the classification process for a sentence from a given text in two stages. We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes). The second approach is given by a voting system based on Naive Bayes and Random Forest. Our system achieves an F1 score of 63.02 % on the subtask evaluation dataset.

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Proceedings of the Qualities of Literary Machine Translation
James Hadley | Maja Popović | Haithem Afli | Andy Way
Proceedings of the Qualities of Literary Machine Translation

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EPITA-ADAPT at SemEval-2019 Task 3: Detecting emotions in textual conversations using deep learning models combination
Abdessalam Bouchekif | Praveen Joshi | Latifa Bouchekif | Haithem Afli
Proceedings of the 13th International Workshop on Semantic Evaluation

Messaging platforms like WhatsApp, Facebook Messenger and Twitter have gained recently much popularity owing to their ability in connecting users in real-time. The content of these textual messages can be a useful resource for text mining to discover and unhide various aspects, including emotions. In this paper we present our submission for SemEval 2019 task ‘EmoContext’. The task consists of classifying a given textual dialogue into one of four emotion classes: Angry, Happy, Sad and Others. Our proposed system is based on the combination of different deep neural networks techniques. In particular, we use Recurrent Neural Networks (LSTM, B-LSTM, GRU, B-GRU), Convolutional Neural Network (CNN) and Transfer Learning (TL) methodes. Our final system, achieves an F1 score of 74.51% on the subtask evaluation dataset.


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FooTweets: A Bilingual Parallel Corpus of World Cup Tweets
Henny Sluyter-Gäthje | Pintu Lohar | Haithem Afli | Andy Way
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Balancing Translation Quality and Sentiment Preservation (Non-archival Extended Abstract)
Pintu Lohar | Haithem Afli | Andy Way
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)


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ADAPT at IJCNLP-2017 Task 4: A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis task
Pintu Lohar | Koel Dutta Chowdhury | Haithem Afli | Mohammed Hasanuzzaman | Andy Way
Proceedings of the IJCNLP 2017, Shared Tasks

In this age of the digital economy, promoting organisations attempt their best to engage the customers in the feedback provisioning process. With the assistance of customer insights, an organisation can develop a better product and provide a better service to its customer. In this paper, we analyse the real world samples of customer feedback from Microsoft Office customers in four languages, i.e., English, French, Spanish and Japanese and conclude a five-plus-one-classes categorisation (comment, request, bug, complaint, meaningless and undetermined) for meaning classification. The task is to %access multilingual corpora annotated by the proposed meaning categorization scheme and develop a system to determine what class(es) the customer feedback sentences should be annotated as in four languages. We propose following approaches to accomplish this task: (i) a multinomial naive bayes (MNB) approach for multi-label classification, (ii) MNB with one-vs-rest classifier approach, and (iii) the combination of the multilabel classification-based and the sentiment classification-based approach. Our best system produces F-scores of 0.67, 0.83, 0.72 and 0.7 for English, Spanish, French and Japanese, respectively. The results are competitive to the best ones for all languages and secure 3rd and 5th position for Japanese and French, respectively, among all submitted systems.

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Identifying Effective Translations for Cross-lingual Arabic-to-English User-generated Speech Search
Ahmad Khwileh | Haithem Afli | Gareth Jones | Andy Way
Proceedings of the Third Arabic Natural Language Processing Workshop

Cross Language Information Retrieval (CLIR) systems are a valuable tool to enable speakers of one language to search for content of interest expressed in a different language. A group for whom this is of particular interest is bilingual Arabic speakers who wish to search for English language content using information needs expressed in Arabic queries. A key challenge in CLIR is crossing the language barrier between the query and the documents. The most common approach to bridging this gap is automated query translation, which can be unreliable for vague or short queries. In this work, we examine the potential for improving CLIR effectiveness by predicting the translation effectiveness using Query Performance Prediction (QPP) techniques. We propose a novel QPP method to estimate the quality of translation for an Arabic-English Cross-lingual User-generated Speech Search (CLUGS) task. We present an empirical evaluation that demonstrates the quality of our method on alternative translation outputs extracted from an Arabic-to-English Machine Translation system developed for this task. Finally, we show how this framework can be integrated in CLUGS to find relevant translations for improved retrieval performance.

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Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora
Haithem Afli | Chao-Hong Liu
Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora

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MultiNews: A Web collection of an Aligned Multimodal and Multilingual Corpus
Haithem Afli | Pintu Lohar | Andy Way
Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora

Integrating Natural Language Processing (NLP) and computer vision is a promising effort. However, the applicability of these methods directly depends on the availability of a specific multimodal data that includes images and texts. In this paper, we present a collection of a Multimodal corpus of comparable texts and their images in 9 languages from the web news articles of Euronews website. This corpus has found widespread use in the NLP community in Multilingual and multimodal tasks. Here, we focus on its acquisition of the images and text data and their multilingual alignment.


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Using SMT for OCR Error Correction of Historical Texts
Haithem Afli | Zhengwei Qiu | Andy Way | Páraic Sheridan
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

A trend to digitize historical paper-based archives has emerged in recent years, with the advent of digital optical scanners. A lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into electronic versions that can be manipulated by a computer. For this purpose, Optical Character Recognition (OCR) systems have been developed to transform scanned digital text into editable computer text. However, different kinds of errors in the OCR system output text can be found, but Automatic Error Correction tools can help in performing the quality of electronic texts by cleaning and removing noises. In this paper, we perform a qualitative and quantitative comparison of several error-correction techniques for historical French documents. Experimentation shows that our Machine Translation for Error Correction method is superior to other Language Modelling correction techniques, with nearly 13% relative improvement compared to the initial baseline.

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The ADAPT Bilingual Document Alignment system at WMT16
Pintu Lohar | Haithem Afli | Chao-Hong Liu | Andy Way
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Integrating Optical Character Recognition and Machine Translation of Historical Documents
Haithem Afli | Andy Way
Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH)

Machine Translation (MT) plays a critical role in expanding capacity in the translation industry. However, many valuable documents, including digital documents, are encoded in non-accessible formats for machine processing (e.g., Historical or Legal documents). Such documents must be passed through a process of Optical Character Recognition (OCR) to render the text suitable for MT. No matter how good the OCR is, this process introduces recognition errors, which often renders MT ineffective. In this paper, we propose a new OCR to MT framework based on adding a new OCR error correction module to enhance the overall quality of translation. Experimentation shows that our new system correction based on the combination of Language Modeling and Translation methods outperforms the baseline system by nearly 30% relative improvement.


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Multimodal Comparable Corpora as Resources for Extracting Parallel Data: Parallel Phrases Extraction
Haithem Afli | Loïc Barrault | Holger Schwenk
Proceedings of the Sixth International Joint Conference on Natural Language Processing


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Traduction automatique à partir de corpus comparables: extraction de phrases parallèles à partir de données comparables multimodales (Automatic Translation from Comparable corpora : extracting parallel sentences from multimodal comparable corpora) [in French]
Haithem Afli | Loïc Barrault | Holger Schwenk
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN


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LIUM’s SMT Machine Translation Systems for WMT 2011
Holger Schwenk | Patrik Lambert | Loïc Barrault | Christophe Servan | Sadaf Abdul-Rauf | Haithem Afli | Kashif Shah
Proceedings of the Sixth Workshop on Statistical Machine Translation