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Dialogue Act Classification On Switchboard

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
DAH-CRF 82.3A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification-
ALDMN81.5Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training-
CNN[[Lee and Dernoncourt2016]]73.1Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks-
Speaker83.2Speaker Turn Modeling for Dialogue Act Classification-
CRF-ASN81.3Dialogue Act Recognition via CRF-Attentive Structured Network-
RNN with 3 utterances in context77.34A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks-
Bi-LSTM-CRF79.2Dialogue Act Sequence Labeling using Hierarchical encoder with CRF-
HGRU + Beam Search + Guided attention85.0Guiding attention in Sequence-to-sequence models for Dialogue Act prediction-
Utt-Att-BiRNN77.42Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks-
Bi-RNN + Self-Attention + Context82.9Dialogue Act Classification with Context-Aware Self-Attention-
Pretrained Hierarchical Transformer79.2Hierarchical Pre-training for Sequence Labelling in Spoken Dialog-
0 of 11 row(s) selected.
Dialogue Act Classification On Switchboard | SOTA | HyperAI