论文标题
这对话连贯吗？从对话行为和实体中学习
Is this Dialogue Coherent? Learning from Dialogue Acts and Entities
论文作者
论文摘要
在这项工作中，我们调查了人类对开放域对话中连贯性的看法。特别是，我们解决了注释和建模下一步候选人的一致性的问题，同时考虑了整个对话的历史。首先，我们创建了打电筒连贯性（SWBD-COH）语料库，这是一个以转向连贯等级注释的人类人类口语对话的数据集，考虑到完整的对话环境，提供了下一步的候选候选话语评分。我们对语料库的统计分析表明，转向连贯感如何受到先前引入的实体分布模式以及所使用的对话行为的影响。其次，我们尝试不同的体系结构，以建模实体，对话行为及其组合，并评估其在预测SWBD-COH的人类相干等级方面的性能。我们发现，结合DA和实体信息的模型产生了响应选择和转向连贯等级的最佳性能。
In this work, we investigate the human perception of coherence in open-domain dialogues. In particular, we address the problem of annotating and modeling the coherence of next-turn candidates while considering the entire history of the dialogue. First, we create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings, where next-turn candidate utterances ratings are provided considering the full dialogue context. Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities previously introduced and the Dialogue Acts used. Second, we experiment with different architectures to model entities, Dialogue Acts and their combination and evaluate their performance in predicting human coherence ratings on SWBD-Coh. We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.
