Matthieu Labeau


2021

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Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional Categories
Gaël Guibon | Matthieu Labeau | Hélène Flamein | Luce Lefeuvre | Chloé Clavel
Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing

In this paper, we place ourselves in a classification scenario in which the target classes and data type are not accessible during training. We use a meta-learning approach to determine whether or not meta-trained information from common social network data with fine-grained emotion labels can achieve competitive performance on messages labeled with different emotion categories. We leverage few-shot learning to match with the classification scenario and consider metric learning based meta-learning by setting up Prototypical Networks with a Transformer encoder, trained in an episodic fashion. This approach proves to be effective for capturing meta-information from a source emotional tag set to predict previously unseen emotional tags. Even though shifting the data type triggers an expected performance drop, our meta-learning approach achieves decent results when compared to the fully supervised one.

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Improving Multimodal fusion via Mutual Dependency Maximisation
Pierre Colombo | Emile Chapuis | Matthieu Labeau | Chloé Clavel
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Multimodal sentiment analysis is a trending area of research, and multimodal fusion is one of its most active topic. Acknowledging humans communicate through a variety of channels (i.e visual, acoustic, linguistic), multimodal systems aim at integrating different unimodal representations into a synthetic one. So far, a consequent effort has been made on developing complex architectures allowing the fusion of these modalities. However, such systems are mainly trained by minimising simple losses such as L1 or cross-entropy. In this work, we investigate unexplored penalties and propose a set of new objectives that measure the dependency between modalities. We demonstrate that our new penalties lead to a consistent improvement (up to 4.3 on accuracy) across a large variety of state-of-the-art models on two well-known sentiment analysis datasets: CMU-MOSI and CMU-MOSEI. Our method not only achieves a new SOTA on both datasets but also produces representations that are more robust to modality drops. Finally, a by-product of our methods includes a statistical network which can be used to interpret the high dimensional representations learnt by the model.

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Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks
Gaël Guibon | Matthieu Labeau | Hélène Flamein | Luce Lefeuvre | Chloé Clavel
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Several recent studies on dyadic human-human interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies dedicated to more precise environments such as after sales services or customer satisfaction surveys. In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow. This context leads to multiple challenges that range from exploiting restricted, small and mostly unlabeled datasets to finding and adapting methods for such context. We tackle these challenges by using Few-Shot Learning while making the hypothesis it can serve conversational emotion classification for different languages and sparse labels. We contribute by proposing a variation of Prototypical Networks for sequence labeling in conversation that we name ProtoSeq. We test this method on two datasets with different languages: daily conversations in English and customer service chat conversations in French. When applied to emotion classification in conversations, our method proved to be competitive even when compared to other ones.

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Code-switched inspired losses for spoken dialog representations
Pierre Colombo | Emile Chapuis | Matthieu Labeau | Chloé Clavel
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Spoken dialogue systems need to be able to handle both multiple languages and multilinguality inside a conversation (e.g in case of code-switching). In this work, we introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations. The goal of these losses is to expose the model to code-switched language. In order to scale up training, we automatically build a pretraining corpus composed of multilingual conversations in five different languages (French, Italian, English, German and Spanish) from OpenSubtitles, a huge multilingual corpus composed of 24.3G tokens. We test the generic representations on MIAM, a new benchmark composed of five dialogue act corpora on the same aforementioned languages as well as on two novel multilingual tasks (i.e multilingual mask utterance retrieval and multilingual inconsistency identification). Our experiments show that our new losses achieve a better performance in both monolingual and multilingual settings.

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Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement (Meta-learning : Classifying Messages into Unseen Emotional Categories)
Gaël Guibon | Matthieu Labeau | Hélène Flamein | Luce Lefeuvre | Chloé Clavel
Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale

Dans cet article nous reproduisons un scénario d’apprentissage selon lequel les données cibles ne sont pas accessibles et seules des données connexes le sont. Nous utilisons une approche par méta-apprentissage afin de déterminer si les méta-informations apprises à partir de messages issus de médias sociaux, finement annotés en émotions, peuvent produire de bonnes performances une fois utilisées sur des messages issus de conversations, étiquetés en émotions avec une granularité différente. Nous mettons à profit l’apprentissage sur quelques exemples (few-shot learning) pour la mise en place de ce scénario. Cette approche se montre efficace pour capturer les méta-informations d’un jeu d’étiquettes émotionnelles pour prédire des étiquettes jusqu’alors inconnues au modèle. Bien que le fait de varier le type de données engendre une baisse de performance, notre approche par méta-apprentissage atteint des résultats décents comparés au référentiel d’apprentissage supervisé.

2020

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The importance of fillers for text representations of speech transcripts
Tanvi Dinkar | Pierre Colombo | Matthieu Labeau | Chloé Clavel
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

While being an essential component of spoken language, fillers (e.g. “um” or “uh”) often remain overlooked in Spoken Language Understanding (SLU) tasks. We explore the possibility of representing them with deep contextualised embeddings, showing improvements on modelling spoken language and two downstream tasks — predicting a speaker’s stance and expressed confidence.

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Hierarchical Pre-training for Sequence Labelling in Spoken Dialog
Emile Chapuis | Pierre Colombo | Matteo Manica | Matthieu Labeau | Chloé Clavel
Findings of the Association for Computational Linguistics: EMNLP 2020

Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.

2019

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Experimenting with Power Divergences for Language Modeling
Matthieu Labeau | Shay B. Cohen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Neural language models are usually trained using Maximum-Likelihood Estimation (MLE). The corresponding objective function for MLE is derived from the Kullback-Leibler (KL) divergence between the empirical probability distribution representing the data and the parametric probability distribution output by the model. However, the word frequency discrepancies in natural language make performance extremely uneven: while the perplexity is usually very low for frequent words, it is especially difficult to predict rare words. In this paper, we experiment with several families (alpha, beta and gamma) of power divergences, generalized from the KL divergence, for learning language models with an objective different than standard MLE. Intuitively, these divergences should affect the way the probability mass is spread during learning, notably by prioritizing performances on high or low-frequency words. In addition, we implement and experiment with various sampling-based objectives, where the computation of the output layer is only done on a small subset of the vocabulary. They are derived as power generalizations of a softmax approximated via Importance Sampling, and Noise Contrastive Estimation, for accelerated learning. Our experiments on the Penn Treebank and Wikitext-2 show that these power divergences can indeed be used to prioritize learning on the frequent or rare words, and lead to general performance improvements in the case of sampling-based learning.

2018

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Algorithmes à base d’échantillonage pour l’entraînement de modèles de langue neuronaux (Here the title in English)
Matthieu Labeau | Alexandre Allauzen
Actes de la Conférence TALN. Volume 1 - Articles longs, articles courts de TALN

L’estimation contrastive bruitée (NCE) et l’échantillonage par importance (IS) sont des procédures d’entraînement basées sur l’échantillonage, que l’on utilise habituellement à la place de l’estimation du maximum de vraisemblance (MLE) pour éviter le calcul du softmax lorsque l’on entraîne des modèles de langue neuronaux. Dans cet article, nous cherchons à résumer le fonctionnement de ces algorithmes, et leur utilisation dans la littérature du TAL. Nous les comparons expérimentalement, et présentons des manières de faciliter l’entraînement du NCE.

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Learning with Noise-Contrastive Estimation: Easing training by learning to scale
Matthieu Labeau | Alexandre Allauzen
Proceedings of the 27th International Conference on Computational Linguistics

Noise-Contrastive Estimation (NCE) is a learning criterion that is regularly used to train neural language models in place of Maximum Likelihood Estimation, since it avoids the computational bottleneck caused by the output softmax. In this paper, we analyse and explain some of the weaknesses of this objective function, linked to the mechanism of self-normalization, by closely monitoring comparative experiments. We then explore several remedies and modifications to propose tractable and efficient NCE training strategies. In particular, we propose to make the scaling factor a trainable parameter of the model, and to use the noise distribution to initialize the output bias. These solutions, yet simple, yield stable and competitive performances in either small and large scale language modelling tasks.

2017

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Représentations continues dérivées des caractères pour un modèle de langue neuronal à vocabulaire ouvert (Opening the vocabulary of neural language models with character-level word representations)
Matthieu Labeau | Alexandre Allauzen
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs

Cet article propose une architecture neuronale pour un modèle de langue à vocabulaire ouvert. Les représentations continues des mots sont calculées à la volée à partir des caractères les composant, gràce à une couche convolutionnelle suivie d’une couche de regroupement (pooling). Cela permet au modèle de représenter n’importe quel mot, qu’il fasse partie du contexte ou soit évalué pour la prédiction. La fonction objectif est dérivée de l’estimation contrastive bruitée (Noise Contrastive Estimation, ou NCE), calculable dans notre cas sans vocabulaire. Nous évaluons la capacité de notre modèle à construire des représentations continues de mots inconnus sur la tâche de traduction automatique IWSLT-2016, de l’Anglais vers le Tchèque, en ré-évaluant les N meilleures hypothèses (N-best reranking). Les résultats expérimentaux permettent des gains jusqu’à 0,7 point BLEU. Ils montrent aussi la difficulté d’utiliser des représentations dérivées des caractères pour la prédiction.

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Adaptation au domaine pour l’analyse morpho-syntaxique (Domain Adaptation for PoS tagging)
Éléonor Bartenlian | Margot Lacour | Matthieu Labeau | Alexandre Allauzen | Guillaume Wisniewski | François Yvon
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts

Ce travail cherche à comprendre pourquoi les performances d’un analyseur morpho-syntaxiques chutent fortement lorsque celui-ci est utilisé sur des données hors domaine. Nous montrons à l’aide d’une expérience jouet que ce comportement peut être dû à un phénomène de masquage des caractéristiques lexicalisées par les caractéristiques non lexicalisées. Nous proposons plusieurs modèles essayant de réduire cet effet.

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An experimental analysis of Noise-Contrastive Estimation: the noise distribution matters
Matthieu Labeau | Alexandre Allauzen
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Noise Contrastive Estimation (NCE) is a learning procedure that is regularly used to train neural language models, since it avoids the computational bottleneck caused by the output softmax. In this paper, we attempt to explain some of the weaknesses of this objective function, and to draw directions for further developments. Experiments on a small task show the issues raised by an unigram noise distribution, and that a context dependent noise distribution, such as the bigram distribution, can solve these issues and provide stable and data-efficient learning.

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Character and Subword-Based Word Representation for Neural Language Modeling Prediction
Matthieu Labeau | Alexandre Allauzen
Proceedings of the First Workshop on Subword and Character Level Models in NLP

Most of neural language models use different kinds of embeddings for word prediction. While word embeddings can be associated to each word in the vocabulary or derived from characters as well as factored morphological decomposition, these word representations are mainly used to parametrize the input, i.e. the context of prediction. This work investigates the effect of using subword units (character and factored morphological decomposition) to build output representations for neural language modeling. We present a case study on Czech, a morphologically-rich language, experimenting with different input and output representations. When working with the full training vocabulary, despite unstable training, our experiments show that augmenting the output word representations with character-based embeddings can significantly improve the performance of the model. Moreover, reducing the size of the output look-up table, to let the character-based embeddings represent rare words, brings further improvement.

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LIMSI@WMT’17
Franck Burlot | Pooyan Safari | Matthieu Labeau | Alexandre Allauzen | François Yvon
Proceedings of the Second Conference on Machine Translation

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Proceedings of ACL 2017, Student Research Workshop
Allyson Ettinger | Spandana Gella | Matthieu Labeau | Cecilia Ovesdotter Alm | Marine Carpuat | Mark Dredze
Proceedings of ACL 2017, Student Research Workshop

2016

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LIMSI@IWSLT’16: MT Track
Franck Burlot | Matthieu Labeau | Elena Knyazeva | Thomas Lavergne | Alexandre Allauzen | François Yvon
Proceedings of the 13th International Conference on Spoken Language Translation

This paper describes LIMSI’s submission to the MT track of IWSLT 2016. We report results for translation from English into Czech. Our submission is an attempt to address the difficulties of translating into a morphologically rich language by paying special attention to the morphology generation on target side. To this end, we propose two ways of improving the morphological fluency of the output: 1. by performing translation and inflection of the target language in two separate steps, and 2. by using a neural language model with characted-based word representation. We finally present the combination of both methods used for our primary system submission.

2015

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Non-lexical neural architecture for fine-grained POS Tagging
Matthieu Labeau | Kevin Löser | Alexandre Allauzen
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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LIMSI@WMT’15 : Translation Task
Benjamin Marie | Alexandre Allauzen | Franck Burlot | Quoc-Khanh Do | Julia Ive | Elena Knyazeva | Matthieu Labeau | Thomas Lavergne | Kevin Löser | Nicolas Pécheux | François Yvon
Proceedings of the Tenth Workshop on Statistical Machine Translation