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    <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 six points and contrasting the final systems.</abstract>
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%0 Conference Proceedings
%T You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions
%A Kabir, Tasnim
%A Sung, Yoo Yeon
%A Bandyopadhyay, Saptarashmi
%A Zou, Hao
%A Chandra, Abhranil
%A Boyd-Graber, Jordan
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kabir-etal-2024-make
%X 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 six points and contrasting the final systems.
%U https://aclanthology.org/2024.emnlp-main.1140
%P 20486-20510

Markdown (Informal)

[You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions](https://aclanthology.org/2024.emnlp-main.1140) (Kabir et al., EMNLP 2024)

• You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions (Kabir et al., EMNLP 2024)

ACL

• Tasnim Kabir, Yoo Yeon Sung, Saptarashmi Bandyopadhyay, Hao Zou, Abhranil Chandra, and Jordan Boyd-Graber. 2024. You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 20486–20510, Miami, Florida, USA. Association for Computational Linguistics.