Bingzhi Li

Doctorant

Enseignement

2020-2021 :     

- Bases programmation, CM niveau M1 
- Langages formels TD niveau M1 
- Projets TAL (L3)

2021-2022:

- Bases programmation, CM niveau M1 
- Langages formels et parsing TD niveau M1 
- Projets TAL (L3)

 

 

Thèse

Titre : Étude des capacités abstractives de modèles de langue neuronaux

Inscription : 2020 à Université de Paris

Bibliographie

Publications:

Bingzhi Li, Guillaume Wisniewski, and Benoit Crabbé 2022a. Assessing the capacity of transformer to abstract syntactic representations: a contrastive analysis based on long-distance agreement. Transactions of the Association for Computational Linguistics

Bingzhi Li, Guillaume Wisniewski, and Benoit Crabbé. 2022b. How Distributed are Distributed Representations? An Observation on the Locality of Syntactic Information in Verb Agreement Tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics,Volume 2, pages 501–507, Dublin, Ireland. Association for Computational Linguistics.

Bingzhi Li, Guillaume Wisniewski, and Benoît Crabbé. 2022c. Les représentations distribuées sont-elles vraiment distribuées ? Observations sur la localisation de l’information syntaxique dans les tâches d’accord du verbe en français. Traitement Automatique des Langues Naturelles, pages 384–391, Avignon, France. ATALA.

Bingzhi Li, Guillaume Wisniewski, and Benoit Crabbé. 2021. Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4599–4610, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.

Bingzhi Li and Guillaume Wisniewski. 2021. Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3080–3089, Online. Association for Computational Linguistics.