Abstract
Reading a document and extracting an answer to a question about its content has attracted substantial attention recently. While most work has focused on the interaction between the question and the document, in this work we evaluate the importance of context when the question and document are processed independently. We take a standard neural architecture for this task, and show that by providing rich contextualized word representations from a large pre-trained language model as well as allowing the model to choose between context-dependent and context-independent word representations, we can obtain dramatic improvements and reach performance comparable to state-of-the-art on the competitive SQuAD dataset.- Anthology ID:
- N18-2088
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 554–559
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/N18-2088/
- DOI:
- 10.18653/v1/N18-2088
- Cite (ACL):
- Shimi Salant and Jonathan Berant. 2018. Contextualized Word Representations for Reading Comprehension. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 554–559, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Contextualized Word Representations for Reading Comprehension (Salant & Berant, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/N18-2088.pdf
- Code
- shimisalant/CWR
- Data
- SQuAD