Papers with DA classification
Annotation Process for the Dialog Act Classification of a Taglish E-commerce Q&A Corpus (D19-51)
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| Challenge: | Existing studies on DA classification in general contexts have not addressed this problem. |
| Approach: | They constructed a text-based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines. |
| Outcome: | The text-based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines was constructed using a tagset for DA classification . the corpus was composed dominantly of single-label posts, with 34% of the corpuse having multiple intent tags. |
Speaker Turn Modeling for Dialogue Act Classification (2021.findings-emnlp)
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| Challenge: | Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, thus treating it no different than non-interactive written text. |
| Approach: | They propose to integrate the turn changes in conversations among speakers when modeling DAs by learning conversation-invariant speaker turn embeddings to represent speaker turns in a conversation. |
| Outcome: | The proposed model captures semantics from the dialogue content while accounting for different speaker turns in a conversation. |
ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents (C18-1)
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| Challenge: | Existing methods for DA annotation are incompatible with each other and do not cover all aspects necessary for open-domain human-machine interaction. |
| Approach: | They propose to map publicly available corpora to a subset of the ISO standard and create a task-independent training corpus for DA classification. |
| Outcome: | The proposed method can train a domain-independent DA tagger on out-of-domain conversational data and achieve robustness across different DA categories. |
COMICORDA: Dialogue Act Recognition in Comic Books (2024.lrec-main)
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| Challenge: | Existing work on dialogue act recognition from images is limited to speech balloon segmentation and optical character recognition. |
| Approach: | They propose a novel DA recognition approach for comic books using speech balloon segmentation, optical character recognition and DA classification. |
| Outcome: | The proposed method achieves 98% average precision for speech balloon segmentation and exceeds 70% accuracy for the DA recognition task. |
Two-level classification for dialogue act recognition in task-oriented dialogues (2020.coling-main)
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| Challenge: | Existing methods for dialogue act classification are limited and feature sets are low . recognizing dialogue acts is useful for identifying type of information and knowledge to be conveyed . |
| Approach: | They propose a 2-level classification technique, distinguishing between generic and specific dialogue acts (DA) they propose an efficient approach for specific DA, based on high-level linguistic features. |
| Outcome: | The proposed method outperforms classical methods for DA classification by including high-level features. |
Hierarchical Fusion for Online Multimodal Dialog Act Classification (2023.findings-emnlp)
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| Challenge: | Existing multimodal DA classification approaches are limited by ineffective audio modeling and late-stage fusion. |
| Approach: | They propose a framework for online multimodal dialog act (DA) classification based on raw audio and ASR-generated transcriptions of current and past utterances. |
| Outcome: | The proposed model achieves a significant increase in the F1 score relative to current state-of-the-art models on two prominent DA classification datasets, MRDA and EMOTyDA. |
Do LLMs Understand Dialogues? A Case Study on Dialogue Acts (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance on many unseen tasks in a zero-shot setting. |
| Approach: | They propose to identify three key pre-tasks essential for accurate DA prediction: Turn Management, Communicative Function Identification, and Dialogue Structure Prediction. |
| Outcome: | The proposed model fails to outperform basic rule-based tasks on three key pre-tasks, and the results suggest that the model is flawed. |