Matching Questions and Answers in Dialogues from Online Forums

TL;DR

This paper introduces a novel QA matching model for dialogues that leverages mutual attention mechanisms and a new dataset, significantly improving accuracy in matching question-answer pairs, especially over long distances.

Contribution

The paper proposes a new QA matching model with mutual attention and creates a dedicated dataset, outperforming existing methods in dialogue QA matching tasks.

Findings

Model outperforms state-of-the-art baselines.

Effective in matching long-distance QA pairs.

New dataset facilitates better evaluation.

Abstract

Matching question-answer relations between two turns in conversations is not only the first step in analyzing dialogue structures, but also valuable for training dialogue systems. This paper presents a QA matching model considering both distance information and dialogue history by two simultaneous attention mechanisms called mutual attention. Given scores computed by the trained model between each non-question turn with its candidate questions, a greedy matching strategy is used for final predictions. Because existing dialogue datasets such as the Ubuntu dataset are not suitable for the QA matching task, we further create a dataset with 1,000 labeled dialogues and demonstrate that our proposed model outperforms the state-of-the-art and other strong baselines, particularly for matching long-distance QA pairs.