Abstract
Language models keep track of complex information about the preceding context – including, e.g., syntactic relations in a sentence. We investigate whether they also capture information beneficial for resolving pronominal anaphora in English. We analyze two state of the art models with LSTM and Transformer architectures, via probe tasks and analysis on a coreference annotated corpus. The Transformer outperforms the LSTM in all analyses. Our results suggest that language models are more successful at learning grammatical constraints than they are at learning truly referential information, in the sense of capturing the fact that we use language to refer to entities in the world. However, we find traces of the latter aspect, too.- Anthology ID:
- 2020.acl-main.384
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4177–4189
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.384
- DOI:
- 10.18653/v1/2020.acl-main.384
- Cite (ACL):
- Ionut-Teodor Sorodoc, Kristina Gulordava, and Gemma Boleda. 2020. Probing for Referential Information in Language Models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4177–4189, Online. Association for Computational Linguistics.
- Cite (Informal):
- Probing for Referential Information in Language Models (Sorodoc et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.acl-main.384.pdf