Identifying Well-formed Natural Language Questions

By Manaal Faruqui, Dipanjan Das. 2018

54 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (2019, background): “(8)BERT achieved very high or superhuman performance on Query Well-Formedness (Faruqui and Das, 2018) and PAWS (Zhang et al.”
Unsupervised Question Decomposition for Question Answering (2020, methodology): “We train a classiﬁer on the question-wellformedness dataset of Faruqui & Das (2018), and we use the classiﬁer to estimate the proportion of sub-questions that are well-formed.”
Unsupervised Question Answering by Cloze Translation (2019, methodology): “We can estimate how well-formed the questions generated by various conﬁgurations of our model are using the Well-formed query dataset of Faruqui and Das (2018).”
Causal Learning in Question Quality Improvement (2019, background): “Afford new dataset to solve question quality improvement problem: The dataset contains three main components: (1) context: text features of questions, (2) treatment: revision suggestions and (3) outcome: indicators of the quality of the revised question.”
What Question Answering can Learn from Trivia Nerds (2019, background): “While this sometimes reflects real world complexities such as actual under-specified or ill-formed search queries (Faruqui and Das, 2018; Kwiatkowski et al., 2019), ignoring this ambiguity is problematic.”
Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement (2023, background): “Actually, the clarity and logical structure of the problem description are crucial factors for human understanding and model comprehension (Shou and Smithson, 2015; Faruqui and Das, 2018; Chu et al., 2020).”