Sahar Ghannay


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Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences
Haithem Afli | Mehwish Alam | Houda Bouamor | Cristina Blasi Casagran | Colleen Boland | Sahar Ghannay
Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences

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Analyzing BERT Cross-lingual Transfer Capabilities in Continual Sequence Labeling
Juan Manuel Coria | Mathilde Veron | Sahar Ghannay | Guillaume Bernard | Hervé Bredin | Olivier Galibert | Sophie Rosset
Proceedings of the First Workshop on Performance and Interpretability Evaluations of Multimodal, Multipurpose, Massive-Scale Models

Knowledge transfer between neural language models is a widely used technique that has proven to improve performance in a multitude of natural language tasks, in particular with the recent rise of large pre-trained language models like BERT. Similarly, high cross-lingual transfer has been shown to occur in multilingual language models. Hence, it is of great importance to better understand this phenomenon as well as its limits. While most studies about cross-lingual transfer focus on training on independent and identically distributed (i.e. i.i.d.) samples, in this paper we study cross-lingual transfer in a continual learning setting on two sequence labeling tasks: slot-filling and named entity recognition. We investigate this by training multilingual BERT on sequences of 9 languages, one language at a time, on the MultiATIS++ and MultiCoNER corpora. Our first findings are that forward transfer between languages is retained although forgetting is present. Additional experiments show that lost performance can be recovered with as little as a single training epoch even if forgetting was high, which can be explained by a progressive shift of model parameters towards a better multilingual initialization. We also find that commonly used metrics might be insufficient to assess continual learning performance.

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The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools
Gaëlle Laperrière | Valentin Pelloin | Antoine Caubrière | Salima Mdhaffar | Nathalie Camelin | Sahar Ghannay | Bassam Jabaian | Yannick Estève
Proceedings of the Thirteenth Language Resources and Evaluation Conference

With the emergence of neural end-to-end approaches for spoken language understanding (SLU), a growing number of studies have been presented during these last three years on this topic. The major part of these works addresses the spoken language understanding domain through a simple task like speech intent detection. In this context, new benchmark datasets have also been produced and shared with the community related to this task. In this paper, we focus on the French MEDIA SLU dataset, distributed since 2005 and used as a benchmark dataset for a large number of research works. This dataset has been shown as being the most challenging one among those accessible to the research community. Distributed by ELRA, this corpus is free for academic research since 2019. Unfortunately, the MEDIA dataset is not really used beyond the French research community. To facilitate its use, a complete recipe, including data preparation, training and evaluation scripts, has been built and integrated to SpeechBrain, an already popular open-source and all-in-one conversational AI toolkit based on PyTorch. This recipe is presented in this paper. In addition, based on the feedback of some researchers who have worked on this dataset for several years, some corrections have been brought to the initial manual annotation: the new version of the data will also be integrated into the ELRA catalogue, as the original one. More, a significant amount of data collected during the construction of the MEDIA corpus in the 2000s was never used until now: we present the first results reached on this subset — also included in the MEDIA SpeechBrain recipe — , that will be used for now as the MEDIA test2. Last, we discuss evaluation issues.

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Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding
Salima Mdhaffar | Valentin Pelloin | Antoine Caubrière | Gaëlle Laperriere | Sahar Ghannay | Bassam Jabaian | Nathalie Camelin | Yannick Estève
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Pretrained models through self-supervised learning have been recently introduced for both acoustic and language modeling. Applied to spoken language understanding tasks, these models have shown their great potential by improving the state-of-the-art performances on challenging benchmark datasets. In this paper, we present an error analysis reached by the use of such models on the French MEDIA benchmark dataset, known as being one of the most challenging benchmarks for the slot filling task among all the benchmarks accessible to the entire research community. One year ago, the state-of-art system reached a Concept Error Rate (CER) of 13.6% through the use of a end-to-end neural architecture. Some months later, a cascade approach based on the sequential use of a fine-tuned wav2vec2.0 model and a fine-tuned BERT model reaches a CER of 11.2%. This significant improvement raises questions about the type of errors that remain difficult to treat, but also about those that have been corrected using these models pre-trained through self-supervision learning on a large amount of data. This study brings some answers in order to better understand the limits of such models and open new perspectives to continue improving the performance.


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Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools
Nesrine Bannour | Sahar Ghannay | Aurélie Névéol | Anne-Laure Ligozat
Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing

Modern Natural Language Processing (NLP) makes intensive use of deep learning methods because of the accuracy they offer for a variety of applications. Due to the significant environmental impact of deep learning, cost-benefit analysis including carbon footprint as well as accuracy measures has been suggested to better document the use of NLP methods for research or deployment. In this paper, we review the tools that are available to measure energy use and CO2 emissions of NLP methods. We describe the scope of the measures provided and compare the use of six tools (carbon tracker, experiment impact tracker, green algorithms, ML CO2 impact, energy usage and cumulator) on named entity recognition experiments performed on different computational set-ups (local server vs. computing facility). Based on these findings, we propose actionable recommendations to accurately measure the environmental impact of NLP experiments.


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A Metric Learning Approach to Misogyny Categorization
Juan Manuel Coria | Sahar Ghannay | Sophie Rosset | Hervé Bredin
Proceedings of the 5th Workshop on Representation Learning for NLP

The task of automatic misogyny identification and categorization has not received as much attention as other natural language tasks have, even though it is crucial for identifying hate speech in social Internet interactions. In this work, we address this sentence classification task from a representation learning perspective, using both a bidirectional LSTM and BERT optimized with the following metric learning loss functions: contrastive loss, triplet loss, center loss, congenerous cosine loss and additive angular margin loss. We set new state-of-the-art for the task with our fine-tuned BERT, whose sentence embeddings can be compared with a simple cosine distance, and we release all our code as open source for easy reproducibility. Moreover, we find that almost every loss function performs equally well in this setting, matching the regular cross entropy loss.

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Neural Networks approaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation Task
Sahar Ghannay | Christophe Servan | Sophie Rosset
Proceedings of the 28th International Conference on Computational Linguistics

In this paper, we present a study on a French Spoken Language Understanding (SLU) task: the MEDIA task. Many works and studies have been proposed for many tasks, but most of them are focused on English language and tasks. The exploration of a richer language like French within the framework of a SLU task implies to recent approaches to handle this difficulty. Since the MEDIA task seems to be one of the most difficult, according several previous studies, we propose to explore Neural Networks approaches focusing of three aspects: firstly, the Neural Network inputs and more specifically the word embeddings; secondly, we compared French version of BERT against the best setup through different ways; Finally, the comparison against State-of-the-Art approaches. Results show that the word embeddings trained on a small corpus need to be updated during SLU model training. Furthermore, the French BERT fine-tuned approaches outperform the classical Neural Network Architectures and achieves state of the art results. However, the contextual embeddings extracted from one of the French BERT approaches achieve comparable results in comparison to word embedding, when integrated into the proposed neural architecture.

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LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis
Somnath Banerjee | Sahar Ghannay | Sophie Rosset | Anne Vilnat | Paolo Rosso
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes the participation of LIMSI_UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text. The proposed approach competed in SentiMix HindiEnglish subtask, that addresses the problem of predicting the sentiment of a given Hindi-English code-mixed tweet. We propose Recurrent Convolutional Neural Network that combines both the recurrent neural network and the convolutional network to better capture the semantics of the text, for code-mixed sentiment analysis. The proposed system obtained 0.69 (best run) in terms of F1 score on the given test data and achieved the 9th place (Codalab username: somban) in the SentiMix Hindi-English subtask.


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Simulating ASR errors for training SLU systems
Edwin Simonnet | Sahar Ghannay | Nathalie Camelin | Yannick Estève
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Utilisation des représentations continues des mots et des paramètres prosodiques pour la détection d’erreurs dans les transcriptions automatiques de la parole (Combining continuous word representation and prosodic features for ASR error detection)
Sahar Ghannay | Yannick Estève | Nathalie Camelin | Camille Dutrey | Fabian Santiago | Martine Adda-Decker
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP

Récemment, l’utilisation des représentations continues de mots a connu beaucoup de succès dans plusieurs tâches de traitement du langage naturel. Dans cet article, nous proposons d’étudier leur utilisation dans une architecture neuronale pour la tâche de détection des erreurs au sein de transcriptions automatiques de la parole. Nous avons également expérimenté et évalué l’utilisation de paramètres prosodiques en suppléments des paramètres classiques (lexicaux, syntaxiques, . . .). La principale contribution de cet article porte sur la combinaison de différentes représentations continues de mots : plusieurs approches de combinaison sont proposées et évaluées afin de tirer profit de leurs complémentarités. Les expériences sont effectuées sur des transcriptions automatiques du corpus ETAPE générées par le système de reconnaissance automatique du LIUM. Les résultats obtenus sont meilleurs que ceux d’un système état de l’art basé sur les champs aléatoires conditionnels. Pour terminer, nous montrons que la mesure de confiance produite est particulièrement bien calibrée selon une évaluation en terme d’Entropie Croisée Normalisée (NCE).

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Word Embedding Evaluation and Combination
Sahar Ghannay | Benoit Favre | Yannick Estève | Nathalie Camelin
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Word embeddings have been successfully used in several natural language processing tasks (NLP) and speech processing. Different approaches have been introduced to calculate word embeddings through neural networks. In the literature, many studies focused on word embedding evaluation, but for our knowledge, there are still some gaps. This paper presents a study focusing on a rigorous comparison of the performances of different kinds of word embeddings. These performances are evaluated on different NLP and linguistic tasks, while all the word embeddings are estimated on the same training data using the same vocabulary, the same number of dimensions, and other similar characteristics. The evaluation results reported in this paper match those in the literature, since they point out that the improvements achieved by a word embedding in one task are not consistently observed across all tasks. For that reason, this paper investigates and evaluates approaches to combine word embeddings in order to take advantage of their complementarity, and to look for the effective word embeddings that can achieve good performances on all tasks. As a conclusion, this paper provides new perceptions of intrinsic qualities of the famous word embedding families, which can be different from the ones provided by works previously published in the scientific literature.

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Evaluation of acoustic word embeddings
Sahar Ghannay | Yannick Estève | Nathalie Camelin | Paul Deleglise
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP


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Using Hypothesis Selection Based Features for Confusion Network MT System Combination
Sahar Ghannay | Loïc Barrault
Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation (HyTra)