Focusing Attention Network for Answer Ranking
Yufei Xie, Shuchun Liu, Tangren Yao, Peng Yao, Zhao Lu · 2019
Answer ranking is an important task in Community Question Answering (CQA), by which “Good” answers should be ranked in the front of “Bad” or “Potentially Useful” answers. The state of the art is the attention-based classification framework that learns the mapping between the questions and the answers. However, we observe that existing attention-based methods perform poorly on complicated question-answer pairs. One major reason is that existing methods cannot get accurate alignments between questions and answers for such pairs. We call the phenomenon “attention divergence”. In this paper, we propose a new attention mechanism, called Focusing Attention Network(FAN), which can automatically draw back the divergent attention by adding the semantic, and metadata features. Our Model can focus on the most important part of the sentence and therefore improve the answer ranking performance. Experimental results on the CQA dataset of SemEval-2016 and SemEval-2017 demonstrate that our method respectively attains 79.38 and 88.72 on MAP and outperforms the Top-1 system in the shared task by 0.19 and 0.29.