Papers › Speaker-change Aware CRF for Dialogue Act Classification

Speaker-change Aware CRF for Dialogue Act Classification

6 Apr 2020COLING 2020 8arXiv:2004.02913archive 2025-07-28

Guokan Shang, Antoine Jean-Pierre Tixier, Michalis Vazirgiannis, Jean-Pierre Lorré

Recent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer. CRF models the conditional probability of the target DA label sequence given the input utterance sequence. However, the task involves another important input sequence, that of speakers, which is ignored by previous work. To address this limitation, this paper proposes a simple modification of the CRF layer that takes speaker-change into account. Experiments on the SwDA corpus show that our modified CRF layer outperforms the original one, with very wide margins for some DA labels. Further, visualizations demonstrate that our CRF layer can learn meaningful, sophisticated transition patterns between DA label pairs conditioned on speaker-change in an end-to-end way. Code is publicly available.

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Code

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Tasks

ClassificationDialogue Act ClassificationGeneral Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Dialogue Act Classification Switchboard Dialog Act Corpus Speaker-change Aware CRF Accuracy 78.7 #1 of 1 Archive leaderboard report
Dialogue Act Classification Switchboard dialogue act corpus Speaker-change Aware CRF Accuracy 78.7 #1 of 1 Archive leaderboard report

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Methods

CRF

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