16 research outputs found
Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer
Conversation structure is useful for both understanding the nature of
conversation dynamics and for providing features for many downstream
applications such as summarization of conversations. In this work, we define
the problem of conversation structure modeling as identifying the parent
utterance(s) to which each utterance in the conversation responds to. Previous
work usually took a pair of utterances to decide whether one utterance is the
parent of the other. We believe the entire ancestral history is a very
important information source to make accurate prediction. Therefore, we design
a novel masking mechanism to guide the ancestor flow, and leverage the
transformer model to aggregate all ancestors to predict parent utterances. Our
experiments are performed on the Reddit dataset (Zhang, Culbertson, and
Paritosh 2017) and the Ubuntu IRC dataset (Kummerfeld et al. 2019). In
addition, we also report experiments on a new larger corpus from the Reddit
platform and release this dataset. We show that the proposed model, that takes
into account the ancestral history of the conversation, significantly
outperforms several strong baselines including the BERT model on all datasetsComment: AAAI 202
Dialogue Coherence Assessment Without Explicit Dialogue Act Labels
Recent dialogue coherence models use the coherence features designed for
monologue texts, e.g. nominal entities, to represent utterances and then
explicitly augment them with dialogue-relevant features, e.g., dialogue act
labels. It indicates two drawbacks, (a) semantics of utterances is limited to
entity mentions, and (b) the performance of coherence models strongly relies on
the quality of the input dialogue act labels. We address these issues by
introducing a novel approach to dialogue coherence assessment. We use dialogue
act prediction as an auxiliary task in a multi-task learning scenario to obtain
informative utterance representations for coherence assessment. Our approach
alleviates the need for explicit dialogue act labels during evaluation. The
results of our experiments show that our model substantially (more than 20
accuracy points) outperforms its strong competitors on the DailyDialogue
corpus, and performs on par with them on the SwitchBoard corpus for ranking
dialogues concerning their coherence.Comment: Accepted at ACL 202