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Dialogue Act Classification On Switchboard

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title	Repository
HGRU + Beam Search + Guided attention	85.0	Guiding attention in Sequence-to-sequence models for Dialogue Act prediction
Speaker	83.2	Speaker Turn Modeling for Dialogue Act Classification
Bi-RNN + Self-Attention + Context	82.9	Dialogue Act Classification with Context-Aware Self-Attention
DAH-CRF	82.3	A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification	-
ALDMN	81.5	Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training	-
CRF-ASN	81.3	Dialogue Act Recognition via CRF-Attentive Structured Network	-
Bi-LSTM-CRF	79.2	Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
Pretrained Hierarchical Transformer	79.2	Hierarchical Pre-training for Sequence Labelling in Spoken Dialog	-
Utt-Att-BiRNN	77.42	Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks
RNN with 3 utterances in context	77.34	A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
CNN[[Lee and Dernoncourt2016]]	73.1	Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

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Dialogue Act Classification On Switchboard | SOTA | HyperAI