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.

本文研究了开放领域对话中人类感知的连贯性问题。我们创建了 SWBD-Coh 语料库，并通过统计分析和不同架构的实验来评估模型的性能，发现模型结合对话行为和实体信息时可以实现最佳性能。