A Neural Model for Dialogue Coherence Assessment

@article{Mesgar2019ANM,
  title={A Neural Model for Dialogue Coherence Assessment},
  author={Mohsen Mesgar and Sebastian B{\"u}cker and Iryna Gurevych},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.08486}
}
Dialogue quality assessment is crucial for evaluating dialogue agents. An essential factor of high-quality dialogues is coherence - what makes dialogue utterances a whole. This paper proposes a novel dialogue coherence model trained in a hierarchical multi-task learning scenario where coherence assessment is the primary and the high-level task, and dialogue act prediction is the auxiliary and the low-level task. The results of our experiments for two benchmark dialogue corpora (i.e. SwitchBoard… CONTINUE READING