Dialogue Act Classification
15 papers with code • 2 benchmarks • 5 datasets
Dialogue act classification is the task of classifying an utterance with respect to the function it serves in a dialogue, i.e. the act the speaker is performing. Dialogue acts are a type of speech acts (for Speech Act Theory, see Austin (1975) and Searle (1969)).
Benchmarks
These leaderboards are used to track progress in Dialogue Act Classification
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Doc2Dial
CPED
Emotional Dialogue Acts
Most implemented papers
Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
ilimugur/short-text-classification
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Dialogue Act recognition associate dialogue acts (i. e., semantic labels) to utterances in a conversation.
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
These neural models annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label.
Probabilistic Word Association for Dialogue Act Classification with Recurrent Neural Networks
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.
A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
Dialogue act recognition is an important part of natural language understanding.
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.
Self-Governing Neural Networks for On-Device Short Text Classification
Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications.
Dialogue Act Classification with Context-Aware Self-Attention
macabdul9/CASA-Dialogue-Act-Classifier
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NAACL 2019
Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks.
Speaker-change Aware CRF for Dialogue Act Classification
guokan_shang/da-classification
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COLING 2020
CRF models the conditional probability of the target DA label sequence given the input utterance sequence.
Contextual Dialogue Act Classification for Open-Domain Conversational Agents
emory-irlab/CDAC
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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.
