Bingzhi Li

Doctorant

0000-0002-1270-9207

Status : PhD student

Address :

LLF, CNRS – UMR 7110
Université Paris Diderot-Paris 7
Case 7031 – 5, rue Thomas Mann,
75205 Paris cedex 13

E-mail : ovatmuv.yv@rgh.h-cnevf.se

General presentation

Interests:

  • computational syntax, linguistic structure 
  • neural language models, syntactic representation, interpretability
  • compositional generalization

Education:

  • MA in Computational linguistics, LLF, Université de Paris, France

Professional experiences:

  • Fall 2022, Visiting researcher at Center for Data Science, NYU, US
    Project: Build a structural generalization benchmark and analyse the generalization properties of Transformers seq2seq models and symbolic models
    Advisors: Tal Linzen, Najoung Kim
  • Spring 2020, research intern at LLF, Université de Paris
    Project: Investigate the representation of tense in BERT
    Advisor: Guillaume Wisniewski

Teaching

  • Formal Grammar and Parsing ( as TA, fall 2020, 2021)
  • Introduction to Python ( main instructor, fall 2020, 2021)
  • L3 and M1 NLP projects ( as co-advisor, spring 2021, 2022)

Thèse

Title : Assessing the abstractive generalization capability of neural language models

Supervision :
  Benoît Crabbé
  Guillaume Wisniewski

Inscription : 2020 à Paris 7

Bibliography

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