Abstract
We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection. We detail the necessary changes to the Question Classification taxonomy and system, the creation of a new Entity Identification system and methods of highlighting entities to achieve this objective. Our experiments show that Question Classes are a strong signal to Deep Learning models for Answer Selection, and enable us to outperform the current state of the art in all variations of our experiments except one. In the best configuration, our MRR and MAP scores outperform the current state of the art by between 3 and 5 points on both versions of the TREC Answer Selection test set, a standard dataset for this task.- Anthology ID:
- C18-1278
- 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:
- 3283–3294
- Language:
- URL:
- https://aclanthology.org/C18-1278
- DOI:
- Cite (ACL):
- Harish Tayyar Madabushi, Mark Lee, and John Barnden. 2018. Integrating Question Classification and Deep Learning for improved Answer Selection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3283–3294, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Integrating Question Classification and Deep Learning for improved Answer Selection (Tayyar Madabushi et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/C18-1278.pdf
- Data
- TrecQA