Graph-Based Tri-Attention Network for Answer Ranking in CQA
- URL: http://arxiv.org/abs/2103.03583v1
- Date: Fri, 5 Mar 2021 10:40:38 GMT
- Title: Graph-Based Tri-Attention Network for Answer Ranking in CQA
- Authors: Wei Zhang, Zeyuan Chen, Chao Dong, Wen Wang, Hongyuan Zha, Jianyong
Wang
- Abstract summary: We propose a novel graph-based tri-attention network, namely GTAN, to generate answer ranking scores.
Experiments on three real-world CQA datasets demonstrate GTAN significantly outperforms state-of-the-art answer ranking methods.
- Score: 56.42018099917321
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In community-based question answering (CQA) platforms, automatic answer
ranking for a given question is critical for finding potentially popular
answers in early times. The mainstream approaches learn to generate answer
ranking scores based on the matching degree between question and answer
representations as well as the influence of respondents. However, they
encounter two main limitations: (1) Correlations between answers in the same
question are often overlooked. (2) Question and respondent representations are
built independently of specific answers before affecting answer
representations. To address the limitations, we devise a novel graph-based
tri-attention network, namely GTAN, which has two innovations. First, GTAN
proposes to construct a graph for each question and learn answer correlations
from each graph through graph neural networks (GNNs). Second, based on the
representations learned from GNNs, an alternating tri-attention method is
developed to alternatively build target-aware respondent representations,
answer-specific question representations, and context-aware answer
representations by attention computation. GTAN finally integrates the above
representations to generate answer ranking scores. Experiments on three
real-world CQA datasets demonstrate GTAN significantly outperforms
state-of-the-art answer ranking methods, validating the rationality of the
network architecture.
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