Abstract
Coreference resolution task demands comprehending a discourse, especially for anaphoric mentions which require semantic information for resolving antecedents. We investigate into how memory networks can be helpful for coreference resolution when posed as question answering problem. The comprehension capability of memory networks assists coreference resolution, particularly for the mentions that require semantic and context information. We experiment memory networks for coreference resolution, with 4 synthetic datasets generated for coreference resolution with varying difficulty levels. Our system’s performance is compared with a traditional coreference resolution system to show why memory network can be promising for coreference resolution.- Anthology ID:
- W17-2605
- Volume:
- Proceedings of the 2nd Workshop on Representation Learning for NLP
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Phil Blunsom, Antoine Bordes, Kyunghyun Cho, Shay Cohen, Chris Dyer, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Yih
- Venue:
- RepL4NLP
- SIG:
- SIGREP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 37–42
- Language:
- URL:
- https://aclanthology.org/W17-2605
- DOI:
- 10.18653/v1/W17-2605
- Cite (ACL):
- Joe Cheri and Pushpak Bhattacharyya. 2017. Towards Harnessing Memory Networks for Coreference Resolution. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 37–42, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Towards Harnessing Memory Networks for Coreference Resolution (Cheri & Bhattacharyya, RepL4NLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-2605.pdf