You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions
Abstract: Training question-answering QA and information retrieval systems for web queries require large, expensive datasets that are difficult to annotate and time-consuming to gather. Moreover, while natural datasets of information-seeking questions are often prone to ambiguity or ill-formed, there are troves of freely available, carefully crafted question datasets for many languages. Thus, we automatically generate shorter, information-seeking questions, resembling web queries in the style of the Natural Questions (NQ) dataset from longer trivia data. Training a QA system on these transformed questions is a viable strategy for alternating to more expensive training setups showing the F1 score difference of less than 6% and contrasting the final systems.
Paper Type: Long
Research Area: Question Answering
Research Area Keywords: Efficient/Low-Resource Methods for NLP, Question Answering, Resources and Evaluation
Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Data resources
Languages Studied: English
Submission Number: 3630
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