Abstract
Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-wellformed categories and report an accuracy of 70.7% on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence models for generating questions for reading comprehension.- Anthology ID:
- D18-1091
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 798–803
- Language:
- URL:
- https://aclanthology.org/D18-1091
- DOI:
- 10.18653/v1/D18-1091
- Cite (ACL):
- Manaal Faruqui and Dipanjan Das. 2018. Identifying Well-formed Natural Language Questions. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 798–803, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Identifying Well-formed Natural Language Questions (Faruqui & Das, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D18-1091.pdf
- Data
- Paralex