论文标题
在线论坛中对话中的对话中的问题和答案
Matching Questions and Answers in Dialogues from Online Forums
论文作者
论文摘要
对话中的两个回合之间的问题 - 答案的关系不仅是分析对话结构的第一步，而且对于培训对话系统来说也很有价值。本文通过两个称为相互关注的同时注意机制来考虑距离信息和对话历史的质量匹配模型。给定由训练有素的模型计算出的分数，每个非疑问转弯都有其候选问题，将贪婪的匹配策略用于最终预测。由于现有的对话数据集（例如Ubuntu数据集）不适合QA匹配任务，因此我们进一步创建了一个具有1,000个标签对话的数据集，并证明我们所提出的模型表现优于先进的艺术和其他强大的基线，尤其是用于匹配长距离QA对。
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.
