Papers by Benoit Crabbé
FrSemCor: Annotating a French Corpus with Supersenses (2020.lrec-1)
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Lucie Barque, Pauline Haas, Richard Huyghe, Delphine Tribout, Marie Candito, Benoit Crabbé, Vincent Segonne
| Challenge: | a new project aims to provide a sense-annotated corpus of French for NLP and linguistics research . the project uses WordNet Unique Beginners as semantic tags to provide interoperability . |
| Approach: | They propose to use WordNet Unique Beginners as semantic tags to annotate French nouns . the project aims to provide a gold standard resource for linguistics and linguistic research . |
| Outcome: | The proposed resource is released online under a Creative Commons license. |
The impact of lexical and grammatical processing on generating code from natural language (2022.findings-acl)
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| Challenge: | Yin and Neubig (2018) identify four key components of importance for natural language to code translation. |
| Approach: | They propose a seq2seq-based architecture that relies on a grammar-based decoder and a lexical substitution component for natural language to code translation. |
| Outcome: | The proposed architecture relies on a grammar-based decoder and a BERT encoder . the proposed architecture is based on lexical substitutions in natural language to code translation . |
On the Relation Between Fine-Tuning, Topological Properties, and Task Performance in Sense-Enhanced Embeddings (2025.acl-long)
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| Challenge: | Enhanced word embeddings do not align well with word senses, resulting in poor performance on word sense identification tasks. |
| Approach: | They propose to use two methods to fine-tune embeddings to identify the topological properties that contribute to sense-enhanced embeddables. |
| Outcome: | The proposed methods improve the embeddings’ ability to capture nuanced semantic distinctions while reducing their expressiveness. |
Contrasting distinct structured views to learn sentence embeddings (2021.eacl-srw)
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| Challenge: | Existing methods to build sentence embeddings rely on a similar Recurrent Neural Network (RNN) heterogeneity of performances across models and tasks makes us assume some structures might be better adapted given the considered task or sentence. |
| Approach: | They propose a self-supervised method that builds sentence embeddings from syntactic structures . they hypothesize that some linguistic representations might be better adapted given the task . |
| Outcome: | The proposed method outperforms comparable methods on several tasks from standard sentence embedding benchmarks. |
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data (2024.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a core component of natural language processing, present in a variety of applications such as medical coding, financial news analysis, or legal documents parsing. |
| Approach: | They propose to use Large Language Models (LLMs) to create NuNER, a compact language representation model specialized in the Named Entity Recognition task. |
| Outcome: | The proposed model outperforms similar-sized foundation models in the few-shot regime and is based on a human-annotated dataset. |
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. |
Unifying Parsing and Tree-Structured Models for Generating Sentence Semantic Representations (2022.naacl-srw)
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| Challenge: | Existing tree-based models require handannotated data to be trained. |
| Approach: | They propose a tree-based model that learns its composition function together with its structure. |
| Outcome: | The proposed model outperforms existing models on downstream tasks and is competitive with Bert base model. |
CodeInsight: A Curated Dataset of Practical Coding Solutions from Stack Overflow (2024.findings-acl)
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| Challenge: | Comprising 3,402 crafted examples, our dataset is designed for both model finetuning and standalone evaluation. |
| Approach: | They propose a dataset that provides examples that include a clarified intent, code snippets associated, and an average of three related unit tests. |
| Outcome: | The proposed dataset includes 3,402 hand-written examples and 3,121 unrefined examples. |
BERTrade: Using Contextual Embeddings to Parse Old French (2022.lrec-1)
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| Challenge: | a growing interest in digital humanities for automatic processing and annotation of historical texts is generating new models for historical languages. |
| Approach: | They use POS-tagging and dependency parsing to evaluate contextual word embedding models . Old French is one of the historical languages for which they have the largest amount of syntactically annotated data . |
| Outcome: | The proposed model can be used to improve performance in Old French, the authors show . they use POS-tagging and dependency parsing to evaluate the model's quality . |
Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement (2021.emnlp-main)
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| Challenge: | Recent studies have shown that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject. |
| Approach: | They propose to take an alternative look at these results by studying whether neural networks are able to build an abstract sentence representation rather than capture surface statistical regularities. |
| Outcome: | The proposed model can achieve high accuracy on the long-range French object-verb agreement, indicating a possible flaw in the model's syntactic ability. |
Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal (D19-1)
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| Challenge: | a variable beam size inference method is proposed for generative parsing for RNNG . the proposed method is not sensitive to lexical biases faced by standard beam search . |
| Approach: | They propose a method of variable beam size inference for Recurrent Neural Network Grammar by drawing inspiration from sequential Monte-Carlo methods such as particle filtering. |
| Outcome: | The proposed method is based on a generative parsing framework that can be used to model brain activity during online sentence comprehension. |
How Many Layers and Why? An Analysis of the Model Depth in Transformers (2021.acl-srw)
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| Challenge: | In deep transformers, weights are tied across layers, resulting in multiple layers. |
| Approach: | They propose a variant of Albert that adapts the number of layers for each token of the input. |
| Outcome: | The proposed model implements the key specificity of Albert and iterates on token representations over time. |
How Distributed are Distributed Representations? An Observation on the Locality of Syntactic Information in Verb Agreement Tasks (2022.acl-short)
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| Challenge: | Using probing, causal analysis and feature selection, we find that syntactic information is encoded locally in the transformers representations consistent with the French grammar. |
| Approach: | They address the question of the localization of syntactic information encoded in transformers representations by probing, causal analysis and feature selection methods. |
| Outcome: | The proposed representations are consistent with the object-past participle agreement in French and are consistent in both languages. |