HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling

TL;DR

This paper introduces HeteroQA, a heterogeneous graph transformer model that leverages multiple information sources in community question answering to improve answer generation, demonstrating superior performance on two datasets.

Contribution

The paper proposes a novel question-aware heterogeneous graph transformer that effectively incorporates multiple information sources in CQA, which was not fully explored in prior methods.

Findings

Outperforms baseline models on two datasets

Effectively integrates diverse information sources

Achieves state-of-the-art results in CQA tasks

Abstract

Community Question Answering (CQA) is a well-defined task that can be used in many scenarios, such as E-Commerce and online user community for special interests. In these communities, users can post articles, give comment, raise a question and answer it. These data form the heterogeneous information sources where each information source have their own special structure and context (comments attached to an article or related question with answers). Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question. However, various types of information sources in the community are not fully explored by these CQA methods and these multiple information sources (MIS) can provide more related knowledge to user's questions. Thus, we propose a question-aware heterogeneous graph transformer to incorporate the MIS in the user community to automatically generate the answer. To evaluate our proposed method, we conduct the experiments on two datasets: $\text{MSM}^{\text{plus}}$ the modified version of benchmark dataset MS-MARCO and the AntQA dataset which is the first large-scale CQA dataset with four types of MIS. Extensive experiments on two datasets show that our model outperforms all the baselines in terms of all the metrics.