Abstract
The paper explores the ability of LSTM networks trained on a language modeling task to detect linguistic structures which are ungrammatical due to extraction violations (extra arguments and subject-relative clause island violations), and considers its implications for the debate on language innatism. The results show that the current RNN model can correctly classify (un)grammatical sentences, in certain conditions, but it is sensitive to linguistic processing factors and probably ultimately unable to induce a more abstract notion of grammaticality, at least in the domain we tested.- Anthology ID:
- C18-1012
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 133–144
- Language:
- URL:
- https://aclanthology.org/C18-1012
- DOI:
- Cite (ACL):
- Shammur Absar Chowdhury and Roberto Zamparelli. 2018. RNN Simulations of Grammaticality Judgments on Long-distance Dependencies. In Proceedings of the 27th International Conference on Computational Linguistics, pages 133–144, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- RNN Simulations of Grammaticality Judgments on Long-distance Dependencies (Chowdhury & Zamparelli, COLING 2018)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/C18-1012.pdf
- Code
- LiCo-TREiL/Computational-Ungrammaticality