Question Answering through Transfer Learning from Large Fine-grained Supervision Data

Sewon Min; Minjoon Seo; Hannaneh Hajishirzi

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arXiv · 1702.02171

Question Answering through Transfer Learning from Large Fine-grained Supervision Data

Sewon Min·Minjoon Seo·Hannaneh Hajishirzi

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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.

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Sewon Min, Minjoon Seo, Hannaneh Hajishirzi. 2017-02-07. Question Answering through Transfer Learning from Large Fine-grained Supervision Data. https://arxiv.org/abs/1702.02171

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