Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy
Deepak Gupta, Rajkumar Pujari, Asif Ekbal, Pushpak Bhattacharyya, Anutosh Maitra, Tom Jain, Shubhashis Sengupta
Abstract
In this paper, we propose a hybrid technique for semantic question matching. It uses a proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning or taxonomy-based knowledge alone.- Anthology ID:
- C18-1042
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 499–513
- Language:
- URL:
- https://aclanthology.org/C18-1042
- DOI:
- Cite (ACL):
- Deepak Gupta, Rajkumar Pujari, Asif Ekbal, Pushpak Bhattacharyya, Anutosh Maitra, Tom Jain, and Shubhashis Sengupta. 2018. Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy. In Proceedings of the 27th International Conference on Computational Linguistics, pages 499–513, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy (Gupta et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/C18-1042.pdf