Papers with dialogue understanding
Generative Induction of Dialogue Task Schemas with Streaming Refinement and Simulated Interactions (2026.tacl-1)
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| Challenge: | Slot Schema Induction (SSI) is a task-oriented dialogue (TOD) system that allows for automatic identification of information slots from unlabeled data. |
| Approach: | They propose a language model that incrementally constructs and refines a slot schema over a stream of dialogue data and then automatically creates high-quality state labels. |
| Outcome: | The proposed method creates high-quality state labels for novel task domains and improves evaluation metrics. |
Curriculum Prompt Learning with Self-Training for Abstractive Dialogue Summarization (2022.emnlp-main)
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| Challenge: | Existing methods to summarize dialogues are difficult due to insufficient training data and low information density. |
| Approach: | They propose a curriculum-based prompt learning method with self-training that gradually increases the degree of prompt perturbation, improving dialogue understanding and modeling capabilities. |
| Outcome: | The proposed model outperforms baseline models on the AMI and ICSI datasets and human evaluations show it is superior in the quality of the summary generation. |
An Adaptive Prompt Generation Framework for Task-oriented Dialogue System (2023.findings-emnlp)
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| Challenge: | Existing black-box large language models (LLMs) have excellent performance in task-oriented dialogue (TOD) tasks, but obtaining suitable prompts for specific tasks is challenging. |
| Approach: | They propose a black-box large language model that generates domain and slot information in the belief state, which serves as prior knowledge for subsequent prompt generation. |
| Outcome: | The proposed framework outperforms existing prompting methods on the MultiWOZ 2.0 dataset. |
CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling (2021.emnlp-main)
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| Challenge: | Existing CSRL parsers struggle to handle conversational structural information. |
| Approach: | They propose a conversational semantic role labeling task which explicitly encodes speaker dependent information and proposes a multi-task learning method to further improve the model. |
| Outcome: | The proposed model outperforms baselines on benchmark datasets on conversation-based tasks. |
Semantic Representation for Dialogue Modeling (2021.acl-long)
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| Challenge: | Existing models for dialogue modeling lack ability to represent core semantics, such as ignoring important entities. |
| Approach: | They develop an algorithm to construct dialogue-level AMR graphs from sentence-level data and explore two ways to incorporate AMRs into dialogue modeling. |
| Outcome: | The proposed model is superior to existing models on dialogue understanding and response generation tasks. |
Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations (2024.acl-long)
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| Challenge: | Existing models for language from a social perspective are gaining popularity . we present a generalizable classification approach that leverages Large Language Models . |
| Approach: | They propose a generalizable classification approach that leverages Large Language Models to detect social meaning in conversations. |
| Outcome: | The proposed approach improves on two social meaning detection tasks over 2,340 settings. |
Medical Dialogue Generation via Dual Flow Modeling (2023.findings-acl)
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| Challenge: | Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription. |
| Approach: | They propose a Dual Flow enhanced Medical (DFMed) dialogue generation framework that extracts the medical entities and doctor's dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow. |
| Outcome: | The proposed framework exceeds baselines in both automatic and manual evaluations on two datasets. |
More Than Spoken Words: Nonverbal Message Extraction and Generation (2023.emnlp-main)
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| Challenge: | Existing studies focus on extracting NMs from small-scale well-structured corpora such as movie scripts wherein NM is enclosed in parentheses by scriptwriters, which greatly decreases the difficulty of extraction. |
| Approach: | They propose to extract nonverbal messages (NMs) from written text and NMs from spoken text by using a semi-supervised learning algorithm. |
| Outcome: | The extracted NMs can generate more relevant, valid, and factually consistent NM than the purely supervised generator. |
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. |