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Question Answering Through Transfer Learning From Large Fine-grained Supervision Data

Sewon Min, Minjoon Seo, Hannaneh Hajishirzi . Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 2017 – 120 citations

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ACL Compositional Generalization Datasets Fine Tuning Interdisciplinary Approaches Question Answering

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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