- Anthology ID:
- 2022.scil-1.6
- Volume:
- Proceedings of the Society for Computation in Linguistics 2022
- Month:
- February
- Year:
- 2022
- Address:
- online
- Editors:
- Allyson Ettinger, Tim Hunter, Brandon Prickett
- Venue:
- SCiL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 76–88
- Language:
- URL:
- https://aclanthology.org/2022.scil-1.6
- DOI:
- Cite (ACL):
- Satoru Ozaki, Dan Yurovsky, and Lori Levin. 2022. How well do LSTM language models learn filler-gap dependencies?. In Proceedings of the Society for Computation in Linguistics 2022, pages 76–88, online. Association for Computational Linguistics.
- Cite (Informal):
- How well do LSTM language models learn filler-gap dependencies? (Ozaki et al., SCiL 2022)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2022.scil-1.6.pdf
- Code
- ikazos/scil2022-fgd
- Data
- Billion Word Benchmark, One Billion Word Benchmark, Penn Treebank