Papers by Maximin Coavoux
FlauBERT: Unsupervised Language Model Pre-training for French (2020.lrec-1)
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Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoit Crabbé, Laurent Besacier, Didier Schwab
| Challenge: | Language models are a key step to achieve state-of-the-art results in many different Natural Language Processing (NLP) tasks. |
| Approach: | They propose to use a language model that is pre-trained on a large and heterogeneous French corpus to train continuous word representations. |
| Outcome: | The proposed model outperforms existing models on a large and heterogeneous French corpus. |
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing (2024.findings-emnlp)
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| Challenge: | Cross-lingual AMR parsing is a task of predicting AMR graphs in a target language when training data is available only in . et al. (2018) evaluated meta-learning for cross-lingual parse in Croatian, Farsi, Korean, Chinese, and French. |
| Approach: | They propose to use meta-learning to tackle cross-lingual AMR parsing in a target language . they evaluate their models in k-shot scenarios and compare them to classical joint learning . |
| Outcome: | The proposed model performs better in 0-shot evaluation for Croatian, Farsi, Korean, Chinese, and French. |
Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech (2024.acl-short)
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| Challenge: | Direct dependency parsing of the speech signal is proposed as a way of incorporating prosodic information into the parser and bypassing the limitations of a pipeline approach. |
| Approach: | They propose to use graph-based parsing and sequence labeling based parses to integrate prosodic information into the parser and bypass limitations of pipeline approaches. |
| Outcome: | The proposed graph based approach outperforms a pipeline approach on a large treebank of spoken french, despite having 30% fewer parameters. |
Limitations of Human Identification of Automatically Generated Text (2024.lrec-main)
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Nadège Alavoine, Maximin Coavoux, Emmanuelle Esperança-Rodier, Romane Gallienne, Carlos Gonzalez Gallardo, Jérôme Goulian, Jose G. Moreno, Aurélie Névéol, Didier Schwab, Vincent Segonne, Johanna Simoens
| Challenge: | Neural text generation tools such as ChatGPT are gaining popularity . human annotations are considered gold standard labels for multiple tasks . |
| Approach: | They propose a new corpus in French and English for recognising automatically generated texts . they propose 'incontext' setup which makes explicit the interaction between two parties . |
| Outcome: | The proposed model generates fluent text, which requires much closer reading than the current model. |
Unsupervised Aspect-Based Multi-Document Abstractive Summarization (D19-54)
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| Challenge: | Existing methods for opinion summarization are expensive and do not deal with contradictory statements. |
| Approach: | They propose an unsupervised abstractive summarization neural system that generates short summaries of reviews in a vector space. |
| Outcome: | The proposed system can generate short summaries of user-generated reviews in a short paragraph, while nobody reads all reviews. |
BERT-Proof Syntactic Structures: Investigating Errors in Discontinuous Constituency Parsing (2021.findings-acl)
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| Challenge: | Recent results show that pretrained language models can be used for many tasks with high accuracy and high performance. |
| Approach: | They propose two methods for automatically analysing discontinuous parsers' errors. |
| Outcome: | The proposed methods characterize errors of a state-of-the-art transition-based discontinuous parser and provide an overview of the contribution of BERT to this task. |
Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains (2024.lrec-main)
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Vincent Segonne, Aidan Mannion, Laura Cristina Alonzo Canul, Alexandre Daniel Audibert, Xingyu Liu, Cécile Macaire, Adrien Pupier, Yongxin Zhou, Mathilde Aguiar, Felix E. Herron, Magali Norré, Massih R Amini, Pierrette Bouillon, Iris Eshkol-Taravella, Emmanuelle Esperança-Rodier, Thomas François, Lorraine Goeuriot, Jérôme Goulian, Mathieu Lafourcade, Benjamin Lecouteux, François Portet, Fabien Ringeval, Vincent Vandeghinste, Maximin Coavoux, Marco Dinarelli, Didier Schwab
| Challenge: | Pretrained language models are the de facto backbone of most state-of-the-art NLP systems. |
| Approach: | They propose a family of domain-specific pretrained PLMs for French focusing on three important domains: transcribed speech, medicine, and law. |
| Outcome: | The proposed models perform better on transcribed speech, medicine, and law domains than state-of-the-art models on a diverse set of tasks and datasets. |
BERT Is Not The Count: Learning to Match Mathematical Statements with Proofs (2023.eacl-main)
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| Challenge: | Existing work on mathematical article analysis uses natural language processing to solve complex mathematical articles. |
| Approach: | They propose a bilinear similarity model and two decoding methods to match statements to proofs effectively. |
| Outcome: | The proposed model matches proofs to statements without being aware of proofs, but it follows a relatively shallow symbolic analysis and matching to achieve that performance. |
What Has LeBenchmark Learnt about French Syntax? (2024.lrec-main)
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| Challenge: | Pretrained acoustic models are increasingly used for downstream speech tasks such as automatic speech recognition, speech translation, spoken language understanding or speech parsing. |
| Approach: | They propose to probing a pretrained acoustic model for French for syntactic information using the Orféo treebank. |
| Outcome: | The proposed model is trained on 7k hours of spoken French and obtained reasonable results on tasks that require higher level linguistic knowledge. |
Self-Supervised and Controlled Multi-Document Opinion Summarization (2021.eacl-main)
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| Challenge: | Existing unsupervised methods for summarizing reviews are based on bootstrapping and require a combination of loss functions or hierarchical latent variables to ensure that the generated summaries remain on-topic. |
| Approach: | They propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. |
| Outcome: | The proposed setup makes training simpler than previous approaches by relying only on standard log-likelihood loss and mainstream models. |
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle (N19-1)
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| Challenge: | Discontinuous constituency trees are derivations of Linear Context-Free Rewriting Systems (LCFRS), which makes them much harder to parse. |
| Approach: | They propose a transition system that uses a set of parsing items with constant-time random access instead of storing subtrees in a stack . |
| Outcome: | The proposed system constructs a discontinuous constituency tree in 4n–2 transitions for a sentence of length n. |
Privacy-preserving Neural Representations of Text (D18-1)
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| Challenge: | a specific type of attack is used to characterize the privacy of neural representations for NLP tasks, in the context of privacy protection. |
| Approach: | They propose several defense methods based on modified training objectives and characterize the tradeoff between privacy and the utility of neural representations. |
| Outcome: | The proposed defenses improve the privacy of neural representations and characterize the tradeoff between privacy and utility of representations. |