Character-Level Question Answering with Attention

David Golub; Xiaodong He

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arXiv · 1604.00727

Character-Level Question Answering with Attention

David Golub·Xiaodong He

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Abstract

We show that a character-level encoder-decoder framework can be successfully applied to question answering with a structured knowledge base. We use our model for single-relation question answering and demonstrate the effectiveness of our approach on the SimpleQuestions dataset (Bordes et al., 2015), where we improve state-of-the-art accuracy from 63.9% to 70.9%, without use of ensembles. Importantly, our character-level model has 16x fewer parameters than an equivalent word-level model, can be learned with significantly less data compared to previous work, which relies on data augmentation, and is robust to new entities in testing.

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David Golub, Xiaodong He. 2016-06-05. Character-Level Question Answering with Attention. https://arxiv.org/abs/1604.00727

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