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        <title>Question Answering through Transfer Learning from Large Fine-grained Supervision Data</title>
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    <abstract>We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset. We achieve the state of the art in two well-studied QA datasets, WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique from SQuAD. For WikiQA, our model outperforms the previous best model by more than 8%. We demonstrate that finer supervision provides better guidance for learning lexical and syntactic information than coarser supervision, through quantitative results and visual analysis. We also show that a similar transfer learning procedure achieves the state of the art on an entailment task.</abstract>
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%0 Conference Proceedings
%T Question Answering through Transfer Learning from Large Fine-grained Supervision Data
%A Min, Sewon
%A Seo, Minjoon
%A Hajishirzi, Hannaneh
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F min-etal-2017-question
%X We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset. We achieve the state of the art in two well-studied QA datasets, WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique from SQuAD. For WikiQA, our model outperforms the previous best model by more than 8%. We demonstrate that finer supervision provides better guidance for learning lexical and syntactic information than coarser supervision, through quantitative results and visual analysis. We also show that a similar transfer learning procedure achieves the state of the art on an entailment task.
%R 10.18653/v1/P17-2081
%U https://aclanthology.org/P17-2081
%U https://doi.org/10.18653/v1/P17-2081
%P 510-517

Markdown (Informal)

[Question Answering through Transfer Learning from Large Fine-grained Supervision Data](https://aclanthology.org/P17-2081) (Min et al., ACL 2017)

• Question Answering through Transfer Learning from Large Fine-grained Supervision Data (Min et al., ACL 2017)

ACL

• Sewon Min, Minjoon Seo, and Hannaneh Hajishirzi. 2017. Question Answering through Transfer Learning from Large Fine-grained Supervision Data. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 510–517, Vancouver, Canada. Association for Computational Linguistics.