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Matching question-answer relations between two turns in conversations is notonly the first step in analyzing dialogue structures, but also valuable fortraining dialogue systems. This paper presents a QA matching model consideringboth distance information and dialogue history by two simultaneous attentionmechanisms called mutual attention. Given scores computed by the trained modelbetween each non-question turn with its candidate questions, a greedy matchingstrategy is used for final predictions. Because existing dialogue datasets suchas the Ubuntu dataset are not suitable for the QA matching task, we furthercreate a dataset with 1,000 labeled dialogues and demonstrate that our proposedmodel outperforms the state-of-the-art and other strong baselines, particularlyfor matching long-distance QA pairs.

Matching Questions and Answers in Dialogues from Online Forums