Yeo Jin Kim, Halim Acosta, Wookhee Min, Jonathan Rowe, Bradford Mott, Snigdha Chaturvedi, James Lester
| Challenge: | Dialogue act recognition is the task of classifying conversational utterances based on their communicative intent or function. |
| Approach: | They propose a dual-processing approach that masks less important tokens in the input and enhances interpretability by using the masks applied during classification learning. |
| Outcome: | The proposed approach significantly improves performance over strong baselines for dialogue act recognition on a collaborative problem-solving dataset and three public dialogue benchmarks. |
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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. |
Dialogue Act Classification with Context-Aware Self-Attention (N19-1)
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| Challenge: | Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. |
| Approach: | They propose a hierarchical deep neural network to model different levels of utterance and dialogue act semantics and use contextual dependencies to improve performance. |
| Outcome: | The proposed model improves on the Switchboard Dialogue Act Corpus while maintaining high accuracy. |
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. |
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Dialogue-act-driven Conversation Model : An Experimental Study (C18-1)
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| Challenge: | In the last decade, natural language processing and machine learning have come a long way towards building an automated dialogue system. |
| Approach: | They propose a way to encode dialogue act information and use it to build a model that can use it in a natural way. |
| Outcome: | The proposed model outperforms baseline models on a new daily dialogue dataset and achieves an MRR of about 84.8%. |
Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging (2020.emnlp-main)
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| Challenge: | Existing studies on DA tagging focus on human-human social conversations, which is less applicable for task-oriented setting. |
| Approach: | They propose a controllable mechanism that augments text input by leveraging the pre-trained Mask token from BERT model. |
| Outcome: | The proposed mechanism augments text input by leveraging the pre-trained Mask token from BERT model. |
Towards Emotion-aided Multi-modal Dialogue Act Classification (2020.acl-main)
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| Challenge: | Considerable work on Dialogue Act Classification (DAC) has been done on textual inputs. |
| Approach: | They propose to use a multimodal Emotion aware Dialogue Act dataset to explore the role of multi-modality and emotion recognition in DAC. |
| Outcome: | The proposed dataset shows that multi-modality and emotion recognition improves DAC performance compared to uni-modal and single task DAC variants. |
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. |
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Act2P: LLM-Driven Online Dialogue Act Classification for Power Analysis (2025.findings-acl)
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| Challenge: | Existing studies focus on explicit utterance functions, overlooking the implicit power dynamics embedded in dialogue. |
| Approach: | They propose an online Dialogue Act Classification and Dynamic Power Analysis framework based on large language models to integrate dialogue act classification with power quantification. |
| Outcome: | The proposed framework outperforms existing methods in online scenarios and shows that dialogue power is distributed and dynamically transferred. |
Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System (2023.findings-acl)
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| Challenge: | End-to-end task-oriented dialogue systems are expensive to annotate and lack data in real scenarios. |
| Approach: | They propose to implement dual learning in task-oriented dialogues to exploit the correlation of heterogeneous data. |
| Outcome: | The proposed method improves the effectiveness of end-to-end task-oriented dialogue systems under multiple benchmarks and obtains state-of-the-art results in low-resource scenarios. |
Planning Like Human: A Dual-process Framework for Dialogue Planning (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) operate in a reactive mode, often resulting in efficiency issues or suboptimal performance. |
| Approach: | They propose a dual-process dialogue planning framework that leverages the dual-process theory of human cognition and a deliberative Monte Carlo Tree Search mechanism to emulate human-like conversational dynamics. |
| Outcome: | The proposed framework outperforms existing methods in achieving high-quality dialogues and operational efficiency. |