Dialogue Act Classification With Context-aware Self-attention | Awesome LLM Papers Add your paper to Awesome LLM Papers

Awesome LLM Papers

A collection of research papers on LLMs.

Stay Updated

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

Dialogue Act Classification With Context-aware Self-attention

Vipul Raheja, Joel Tetreault . Arxiv 2019 – 62 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
Compositional Generalization Datasets Few Shot Interdisciplinary Approaches Neural Machine Translation Variational Autoencoders

Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network. We conduct extensive evaluations on standard Dialogue Act classification datasets and show significant improvement over state-of-the-art results on the Switchboard Dialogue Act (SwDA) Corpus. We also investigate the impact of different utterance-level representation learning methods and show that our method is effective at capturing utterance-level semantic text representations while maintaining high accuracy.

Similar Work