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natural-language-processing-7

Dialogue Act Classification

3260 papers • 126 benchmarks • 313 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)).

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Benchmarks

These leaderboards are used to track progress in dialogue-act-classification-14

Trend

Dataset

Best Model

Actions

Switchboard corpus

Switchboard corpus

ICSI Meeting Recorder Dialog Act (MRDA) corpus

ICSI Meeting Recorder Dialog Act (MRDA) corpus

Libraries

i

Use these libraries to find dialogue-act-classification-14 models and implementations

Datasets

doc2dial

Switchboard-1 Corpus

Switchboard-1 Corpus

CPED

MRDA

Emotional Dialogue Acts

Emotional Dialogue Acts

EMOTyDA

Subtasks

No subtasks available.

Most implemented papers

Speaker-change Aware CRF for Dialogue Act Classification

A. Tixier, M. Vazirgiannis, Guokan Shang, J. Lorré•Sun Apr 05 2020

Experiments on the SwDA corpus show that the modified CRF layer outperforms the original one, with very wide margins for some DA labels, and visualizations demonstrate that the layer can learn meaningful, sophisticated transition patterns between DA label pairs conditioned on speaker-change in an end-to-end way.

17

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Switchboard dialogue act corpus

Switchboard dialogue act corpus

Switchboard Dialog Act Corpus

Switchboard Dialog Act Corpus

EMOTyDA

EMOTyDA

SDN

Switchboard Dialog Act Corpus

Switchboard Dialog Act Corpus

0

Dialogue Act Sequence Labeling using Hierarchical encoder with CRF

Sachindra Joshi, Harshit Kumar, Arvind Agarwal, Riddhiman Dasgupta•Tue Sep 12 2017

A hierarchical recurrent neural network is built using bidirectional LSTM as a base unit and the conditional random field as the top layer to classify each utterance into its corresponding dialogue act, thus modeling the dependency among both, labels and utterances, an important consideration of natural dialogue.

135 0

EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators

Chandrakant Bothe, C. Weber, S. Magg, Stefan Wermter•Sun Dec 01 2019

A pool of various recurrent neural models trained on a dialogue act corpus are proposed to annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label.

12 0

A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments

G. Sukthankar, A. Enayet•Mon Nov 09 2020

This paper presents a transfer learning approach for performing dialogue act classification on issue comments, and compares the performance of several word and sentence level encoding models including Global Vectors for Word Representations, Universal Sentence Encoder, and Bidirectional Encoder Representations from Transformers.

9 0

Probabilistic Word Association for Dialogue Act Classification with Recurrent Neural Networks

Nathan Duran, Steve Battle•Sun Sep 02 2018

A novel probabilistic method of utterance representation is presented and a RNN sentence model for out-of-context DA Classification is described and generated from keywords selected for their frequency association with certain DA’s.

9 0

NatCS: Eliciting Natural Customer Support Dialogues

Saab Mansour, James Gung, Emily Moeng, Wesley Rose, Arshit Gupta•Wed May 03 2023

This work introduces NatCS, a multi-domain collection of spoken customer service conversations based on natural language phenomena observed in real conversations, and demonstrates that dialogue act annotations in NatCS provide more effective training data for modeling real conversations compared to existing synthetic written datasets.

6 0

A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks

Chandrakant Bothe, C. Weber, S. Magg, Stefan Wermter•Mon Apr 30 2018

This work proposes a novel context-based learning method to classify dialogue acts using a character-level language model utterance representation, and shows significant improvement in dialogue act detection.

41 0

Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks

Chandrakant Bothe, C. Weber, S. Magg, Stefan Wermter•Tue May 15 2018

An utterance-level attention-based bidirectional recurrent neural network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances to classify the current one and to show that context-based learning not only improves the performance but also achieves higher confidence in the classification.

17 0

Self-Governing Neural Networks for On-Device Short Text Classification

Zornitsa Kozareva, Sujith Ravi•Sun Dec 31 2017

The findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy, and extensive evaluation on dialog act classification shows significant improvement over state-of-the-art results.

38 0

Dialogue Act Classification with Context-Aware Self-Attention

Joel R. Tetreault, Vipul Raheja•Wed Apr 03 2019

This work exploits the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network to solve the Dialogue Act classification problem as a sequence labeling problem using hierarchical deep neural networks.

119 0

Adding a benchmark result helps the community track progress.

Dialogue Act Classification | State-of-the-Art