Published on Fri Mar 05 2021

Graph-Based Tri-Attention Network for Answer Ranking in CQA

Wei Zhang, Zeyuan Chen, Chao Dong, Wen Wang, Hongyuan Zha, Jianyong Wang
0
0
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.

Tue Apr 07 2020
NLP
Is Graph Structure Necessary for Multi-hop Question Answering?
In this paper, we investigate whether the graph structure is necessary for multi-hop question answering. Our analysis is centered on HotpotQA. We point out that both graph structure and adjacency matrix are not necessary for task-related prior knowledge.
0
0
0
Sat Jun 01 2019
NLP
Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems
In the area of community question answering (CQA), answer selection and answer ranking are two tasks which are applied to help users quickly access valuable answers. Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A) We propose a unified
0
0
0
Sat Nov 16 2019
Machine Learning
An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms
This paper addresses the question of identifying the best candidate answer to a question on Community Question Answer (CQA) forums. We develop a novel induced relational graph convolutional network framework to address the question.
0
0
0
Tue Dec 17 2019
Artificial Intelligence
Knowledge-Enhanced Attentive Learning for Answer Selection in Community Question Answering Systems
In the community question answering (CQA) system, the answer selection task aims to identify the best answer for a specific question. We propose the Knowledge-enhanced Attentive Answer Selection (KAAS) model, which considers both the expertise and the authority of the answerer. We incorporate an external knowledge graph to capture more professional information for vertical CQA systems.
0
0
0
Mon Apr 12 2021
NLP
Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge
0
0
0
Fri Oct 18 2019
Machine Learning
Relational Graph Representation Learning for Open-Domain Question Answering
The experiments suggest that our model achieves state-of-the-art on the WebQuestionsSP benchmark. Our model can learn contextual representation by jointly learning and updating the query, knowledge graph, and document representations.
0
0
0