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)).

Most implemented papers

Dialogue Act Sequence Labeling using Hierarchical encoder with CRF

ilimugur/short-text-classification 13 Sep 2017

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

bothe/EDAs LREC 2020

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

NathanDuran/Probabilistic-RNN-DA-Classifier Engineering Applications of Neural Networks 2018

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

bothe/dialogue-act-recognition 16 May 2018

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

glicerico/SGNN EMNLP 2018

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 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 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 28 May 2020

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

OSU-slatelab/vp-pairwise 28 Oct 2020

Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels.