Dialogue Understanding
29 papers with code • 0 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Dialogue Understanding
Datasets
Doc2Dial
ProsocialDialog
DiaASQ
WDC-Dialogue
Emotional Dialogue Acts
The Mafia Dataset
Latest papers with no code
Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models
We discuss the capabilities of LLMs to utilize deceptive long-horizon conversations between six human players to determine each player's goal and motivation.
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models
Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these intricate contexts.
Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models
A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference for the patient.
U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation
In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios.
Is ChatGPT Equipped with Emotional Dialogue Capabilities?
This report presents a study on the emotional dialogue capability of ChatGPT, an advanced language model developed by OpenAI.
A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention.
Friend-training: Learning from Models of Different but Related Tasks
Current self-training methods such as standard self-training, co-training, tri-training, and others often focus on improving model performance on a single task, utilizing differences in input features, model architectures, and training processes.
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings.
Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation
To fully leverage the visual information for both scene understanding and dialogue generation, we propose the scene-aware prompt for the MDUG task.
Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia
In this work, we analyze the effect of speaker role on language use through the game of Mafia, in which participants are assigned either an honest or a deceptive role.
