Denis Paperno


2020

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Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches
Lin Li | Kees van Deemter | Denis Paperno
Proceedings of the 19th Chinese National Conference on Computational Linguistics

This paper presents our work in long and short form choice, a significant question of lexical choice, which plays an important role in many Natural Language Understanding tasks. Long and short form sharing at least one identical word meaning but with different number of syllables is a highly frequent linguistic phenomenon in Chinese like 老虎-虎(laohu-hu, tiger)

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Génération automatique de définitions pour le français (Definition Modeling in French)
Timothee Mickus | Mathieu Constant | Denis Paperno
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

La génération de définitions est une tâche récente qui vise à produire des définitions lexicographiques à partir de plongements lexicaux. Nous remarquons deux lacunes : (i) l’état de l’art actuel ne s’est penché que sur l’anglais et le chinois, et (ii) l’utilisation escomptée en tant que méthode d’évaluation des plongements lexicaux doit encore être vérifiée. Pour y remédier, nous proposons un jeu de données pour la génération de définitions en français, ainsi qu’une évaluation des performances d’un modèle de génération de définitions simple selon les plongements lexicaux fournis en entrée.

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Geo-Aware Image Caption Generation
Sofia Nikiforova | Tejaswini Deoskar | Denis Paperno | Yoad Winter
Proceedings of the 28th International Conference on Computational Linguistics

Standard image caption generation systems produce generic descriptions of images and do not utilize any contextual information or world knowledge. In particular, they are unable to generate captions that contain references to the geographic context of an image, for example, the location where a photograph is taken or relevant geographic objects around an image location. In this paper, we develop a geo-aware image caption generation system, which incorporates geographic contextual information into a standard image captioning pipeline. We propose a way to build an image-specific representation of the geographic context and adapt the caption generation network to produce appropriate geographic names in the image descriptions. We evaluate our system on a novel captioning dataset that contains contextualized captions and geographic metadata and achieve substantial improvements in BLEU, ROUGE, METEOR and CIDEr scores. We also introduce a new metric to assess generated geographic references directly and empirically demonstrate our system’s ability to produce captions with relevant and factually accurate geographic referencing.

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What Meaning-Form Correlation Has to Compose With: A Study of MFC on Artificial and Natural Language
Timothee Mickus | Timothée Bernard | Denis Paperno
Proceedings of the 28th International Conference on Computational Linguistics

Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive. We focus on the proposal that compositionality can be assessed by measuring meaning-form correlation. We analyze meaning-form correlation on three sets of languages: (i) artificial toy languages tailored to be compositional, (ii) a set of English dictionary definitions, and (iii) a set of English sentences drawn from literature. We find that linguistic phenomena such as synonymy and ungrounded stop-words weigh on MFC measurements, and that straightforward methods to mitigate their effects have widely varying results depending on the dataset they are applied to. Data and code are made publicly available.

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What do you mean, BERT?
Timothee Mickus | Denis Paperno | Mathieu Constant | Kees van Deemter
Proceedings of the Society for Computation in Linguistics 2020

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Limitations in learning an interpreted language with recurrent models
Denis Paperno
Proceedings of the Society for Computation in Linguistics 2020

2019

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Distributional Effects of Gender Contrasts Across Categories
Timothee Mickus | Olivier Bonami | Denis Paperno
Proceedings of the Society for Computation in Linguistics (SCiL) 2019

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Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling
Timothee Mickus | Denis Paperno | Matthieu Constant
Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing

Defining words in a textual context is a useful task both for practical purposes and for gaining insight into distributed word representations. Building on the distributional hypothesis, we argue here that the most natural formalization of definition modeling is to treat it as a sequence-to-sequence task, rather than a word-to-sequence task: given an input sequence with a highlighted word, generate a contextually appropriate definition for it. We implement this approach in a Transformer-based sequence-to-sequence model. Our proposal allows to train contextualization and definition generation in an end-to-end fashion, which is a conceptual improvement over earlier works. We achieve state-of-the-art results both in contextual and non-contextual definition modeling.

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Choosing between Long and Short Word Forms in Mandarin
Lin Li | Kees van Deemter | Denis Paperno | Jingyu Fan
Proceedings of the 12th International Conference on Natural Language Generation

Between 80% and 90% of all Chinese words have long and short form such as 老虎/虎 (lao-hu/hu , tiger) (Duanmu:2013). Consequently, the choice between long and short forms is a key problem for lexical choice across NLP and NLG. Following an earlier work on abbreviations in English (Mahowald et al, 2013), we bring a probabilistic perspective to these questions, using both a behavioral and a corpus-based approach. We hypothesized that there is a higher probability of choosing short form in supportive context than in neutral context in Mandarin. Consistent with our prediction, our findings revealed that predictability of contexts makes effect on speakers’ long and short form choice.

2018

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SemEval-2018 Task 10: Capturing Discriminative Attributes
Alicia Krebs | Alessandro Lenci | Denis Paperno
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes the SemEval 2018 Task 10 on Capturing Discriminative Attributes. Participants were asked to identify whether an attribute could help discriminate between two concepts. For example, a successful system should determine that ‘urine’ is a discriminating feature in the word pair ‘kidney’, ‘bone’. The aim of the task is to better evaluate the capabilities of state of the art semantic models, beyond pure semantic similarity. The task attracted submissions from 21 teams, and the best system achieved a 0.75 F1 score.

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Limitations in learning an interpreted language with recurrent models
Denis Paperno
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

In this submission I report work in progress on learning simplified interpreted languages by means of recurrent models. The data is constructed to reflect core properties of natural language as modeled in formal syntax and semantics. Preliminary results suggest that LSTM networks do generalise to compositional interpretation, albeit only in the most favorable learning setting.

2016

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Typology of Adjectives Benchmark for Compositional Distributional Models
Daria Ryzhova | Maria Kyuseva | Denis Paperno
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper we present a novel application of compositional distributional semantic models (CDSMs): prediction of lexical typology. The paper introduces the notion of typological closeness, which is a novel rigorous formalization of semantic similarity based on comparison of multilingual data. Starting from the Moscow Database of Qualitative Features for adjective typology, we create four datasets of typological closeness, on which we test a range of distributional semantic models. We show that, on the one hand, vector representations of phrases based on data from one language can be used to predict how words within the phrase translate into different languages, and, on the other hand, that typological data can serve as a semantic benchmark for distributional models. We find that compositional distributional models, especially parametric ones, perform way above non-compositional alternatives on the task.

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The LAMBADA dataset: Word prediction requiring a broad discourse context
Denis Paperno | Germán Kruszewski | Angeliki Lazaridou | Ngoc Quan Pham | Raffaella Bernardi | Sandro Pezzelle | Marco Baroni | Gemma Boleda | Raquel Fernández
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Capturing Discriminative Attributes in a Distributional Space: Task Proposal
Alicia Krebs | Denis Paperno
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

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When Hyperparameters Help: Beneficial Parameter Combinations in Distributional Semantic Models
Alicia Krebs | Denis Paperno
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

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Squibs: When the Whole Is Less Than the Sum of Its Parts: How Composition Affects PMI Values in Distributional Semantic Vectors
Denis Paperno | Marco Baroni
Computational Linguistics, Volume 42, Issue 2 - June 2016

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There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics
Germán Kruszewski | Denis Paperno | Raffaella Bernardi | Marco Baroni
Computational Linguistics, Volume 42, Issue 4 - December 2016

2015

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Distributional Semantics in Use
Raffaella Bernardi | Gemma Boleda | Raquel Fernández | Denis Paperno
Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics

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Leveraging Preposition Ambiguity to Assess Compositional Distributional Models of Semantics
Samuel Ritter | Cotie Long | Denis Paperno | Marco Baroni | Matthew Botvinick | Adele Goldberg
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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Deriving Boolean structures from distributional vectors
German Kruszewski | Denis Paperno | Marco Baroni
Transactions of the Association for Computational Linguistics, Volume 3

Corpus-based distributional semantic models capture degrees of semantic relatedness among the words of very large vocabularies, but have problems with logical phenomena such as entailment, that are instead elegantly handled by model-theoretic approaches, which, in turn, do not scale up. We combine the advantages of the two views by inducing a mapping from distributional vectors of words (or sentences) into a Boolean structure of the kind in which natural language terms are assumed to denote. We evaluate this Boolean Distributional Semantic Model (BDSM) on recognizing entailment between words and sentences. The method achieves results comparable to a state-of-the-art SVM, degrades more gracefully when less training data are available and displays interesting qualitative properties.

2014

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A practical and linguistically-motivated approach to compositional distributional semantics
Denis Paperno | Nghia The Pham | Marco Baroni
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)