Dialogue Act Classification
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Benchmarks
Switchboard corpus
EMOTyDA
Most implemented
Speaker-change Aware CRF for Dialogue Act Classification
Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
NatCS: Eliciting Natural Customer Support Dialogues
A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments
Hierarchical Fusion for Online Multimodal Dialog Act Classification
Papers
Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings
Large language models (LLMs) have demonstrated remarkable capabilities in handling complex dialogue tasks without requiring use case-specific fine-tuning. However, analyzing live dialogues in real-time necessitates low-l…
Dialogue Act ClassificationDialogue State TrackingIntent DetectionLow-latency processingHierarchical Fusion for Online Multimodal Dialog Act Classification
We propose a framework for online multimodal dialog act (DA) classification based on raw audio and ASR-generated transcriptions of current and past utterances. Existing multimodal DA classification approaches are limited…
ClassificationDialog Act ClassificationDialogue Act ClassificationInterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations
While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool offering a conversational interface. Such …
Dialogue Act ClassificationHate Speech DetectionQuestion AnsweringTask Selection and Assignment for Multi-modal Multi-task Dialogue Act Classification with Non-stationary Multi-armed Bandits
Multi-task learning (MTL) aims to improve the performance of a primary task by jointly learning with related auxiliary tasks. Traditional MTL methods select tasks randomly during training. However, both previous studies …
Dialogue Act ClassificationMulti-Armed BanditsMulti-Task LearningThompson SamplingEnd-to-end spoken language understanding using joint CTC loss and self-supervised, pretrained acoustic encoders
It is challenging to extract semantic meanings directly from audio signals in spoken language understanding (SLU), due to the lack of textual information. Popular end-to-end (E2E) SLU models utilize sequence-to-sequence …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Dialogue Act Classificationspeech-recognition+2NatCS: Eliciting Natural Customer Support Dialogues
Despite growing interest in applications based on natural customer support conversations, there exist remarkably few publicly available datasets that reflect the expected characteristics of conversations in these setting…
Dialogue Act Classification