Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension

David Golub; Po-Sen Huang; Xiaodong He; Li Deng

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arXiv · 1706.09789

Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension

David Golub·Po-Sen Huang·Xiaodong He·Li Deng

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Abstract

We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network (SynNet). Given a high-performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed SynNet with a pretrained model from the SQuAD dataset on the challenging NewsQA dataset, we achieve an F1 measure of 44.3% with a single model and 46.6% with an ensemble, approaching performance of in-domain models (F1 measure of 50.0%) and outperforming the out-of-domain baseline of 7.6%, without use of provided annotations.

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David Golub, Po-Sen Huang, Xiaodong He, Li Deng. 2017-09-23. Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension. https://arxiv.org/abs/1706.09789

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