33 research outputs found
Thread Reconstruction in Conversational Data using Neural Coherence Models
Discussion forums are an important source of information. They are often used
to answer specific questions a user might have and to discover more about a
topic of interest. Discussions in these forums may evolve in intricate ways,
making it difficult for users to follow the flow of ideas. We propose a novel
approach for automatically identifying the underlying thread structure of a
forum discussion. Our approach is based on a neural model that computes
coherence scores of possible reconstructions and then selects the highest
scoring, i.e., the most coherent one. Preliminary experiments demonstrate
promising results outperforming a number of strong baseline methods.Comment: Neu-IR: Workshop on Neural Information Retrieval 201
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