Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering

Wei Li; Yunfang Wu

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arXiv · 1709.05599

Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering

Wei Li·Yunfang Wu

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Abstract

In this paper, we focus on the problem of answer triggering ad-dressed by Yang et al. (2015), which is a critical component for a real-world question answering system. We employ a hierarchical gated recurrent neural tensor (HGRNT) model to capture both the context information and the deep in-teractions between the candidate answers and the question. Our result on F val-ue achieves 42.6%, which surpasses the baseline by over 10 %.

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Wei Li, Yunfang Wu. 2017-09-17. Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering. https://arxiv.org/abs/1709.05599

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