Dialogue Act Classification
15 papers with code • 2 benchmarks • 5 datasets
These neural models annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label.
The identification of Dialogue Act’s (DA) is an important aspect in determining the meaning of an utterance for many applications that require natural language understanding, and recent work using recurrent neural networks (RNN) has shown promising results when applied to the DA classification problem.
Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks
Recent approaches for dialogue act recognition have shown that context from preceding utterances is important to classify the subsequent one.
Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications.
Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks.
Furthermore, our results show that fine-tuning the CDAC model on a small sample of manually labeled human-machine conversations allows CDAC to more accurately predict dialogue acts in real users' conversations, suggesting a promising direction for future improvements.
Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation
Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels.