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 %.
Cite
@article{arxiv.1709.05599,
title = {Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering},
author = {Wei Li and Yunfang Wu},
journal= {arXiv preprint arXiv:1709.05599},
year = {2017}
}