Identifying Well-formed Natural Language Questions

Manaal Faruqui, Dipanjan Das


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/D18-1091.pdf
Data
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