Dialogue Act Classification
20 papers with code • 4 benchmarks • 7 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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Datasets
Doc2Dial
CPED
Emotional Dialogue Acts
Most implemented papers
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.
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.
A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments
ayeshaEnayet/Dialogue-Act-for-dialogue-analysis-
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Social coding platforms, such as GitHub, serve as laboratories for studying collaborative problem solving in open source software development; a key feature is their ability to support issue reporting which is used by teams to discuss tasks and ideas.
NatCS: Eliciting Natural Customer Support Dialogues
Existing task-oriented dialogue datasets, which were collected to benchmark dialogue systems mainly in written human-to-bot settings, are not representative of real customer support conversations and do not provide realistic benchmarks for systems that are applied to natural data.
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.
