Papers Dialogue Understanding
“Dialogue Understanding” 태그가 달린 논문 79편 · 필터 해제
EditIQ: Automated Cinematic Editing of Static Wide-Angle Videos via Dialogue Interpretation and Saliency Cues
We present EditIQ, a completely automated framework for cinematically editing scenes captured via a stationary, large field-of-view and high-resolution camera. From the static camera feed, EditIQ initially generates mult…
Dialogue InterpretationDialogue UnderstandingLanguage ModelingLanguage Modelling+3What is a Social Media Bot? A Global Comparison of Bot and Human Characteristics
Chatter on social media is 20% bots and 80% humans. Chatter by bots and humans is consistently different: bots tend to use linguistic cues that can be easily automated while humans use cues that require dialogue understa…
Dialogue UnderstandingEnhancing Persona Classification in Dialogue Systems: A Graph Neural Network Approach
In recent years, Large Language Models (LLMs) gain considerable attention for their potential to enhance personalized experiences in virtual assistants and chatbots. A key area of interest is the integration of personas …
ClassificationDialogue UnderstandingGraph Neural NetworkBenchmarking Open-ended Audio Dialogue Understanding for Large Audio-Language Models
Large Audio-Language Models (LALMs) have unclocked audio dialogue capabilities, where audio dialogues are a direct exchange of spoken language between LALMs and humans. Recent advances, such as GPT-4o, have enabled LALMs…
BenchmarkingDialogue UnderstandingBuilding Dialogue Understanding Models for Low-resource Language Indonesian from Scratch
Making use of off-the-shelf resources of resource-rich languages to transfer knowledge for low-resource languages raises much attention recently. The requirements of enabling the model to reach the reliable performance l…
Cross-Lingual TransferDecoderDialogue Understandingintent-classification+4Policy-driven Knowledge Selection and Response Generation for Document-grounded Dialogue
Document-grounded dialogue (DGD) uses documents as external knowledge for dialogue generation. Correctly understanding the dialogue context is crucial for selecting knowledge from the document and generating proper respo…
Dialogue GenerationDialogue UnderstandingResponse GenerationFrom Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs
Recent advancements in large language models have significantly improved their context windows, yet challenges in effective long-term memory management remain. We introduce MemTree, an algorithm that leverages a dynamic,…
Dialogue UnderstandingManagementQuestion AnsweringRetrievalA Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding
Dialogue State Tracking (DST) is crucial for understanding user needs and executing appropriate system actions in task-oriented dialogues. Majority of existing DST methods are designed to work within predefined ontologie…
Dialogue State TrackingDialogue Understandingdomain classificationQuestion AnsweringVisualizing Dialogues: Enhancing Image Selection through Dialogue Understanding with Large Language Models
Recent advancements in dialogue systems have highlighted the significance of integrating multimodal responses, which enable conveying ideas through diverse modalities rather than solely relying on text-based interactions…
Dialogue UnderstandingImage RetrievalRetrievalLeveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations
We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extrac…
Dialogue Understandingdomain classificationInvestigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets
In spoken Task-Oriented Dialogue (TOD) systems, the choice of the semantic representation describing the users' requests is key to a smooth interaction. Indeed, the system uses this representation to reason over a databa…
Dialogue UnderstandingLanguage ModelingLanguage ModellingLarge Language ModelDialSim: A Real-Time Simulator for Evaluating Long-Term Multi-Party Dialogue Understanding of Conversational Agents
Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of conversational agents, making them applicable to various fields (e.g., education). Despite their progress, the evaluatio…
Dialogue UnderstandingQuestion AnsweringSD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Words
Speech encompasses a wealth of information, including but not limited to content, paralinguistic, and environmental information. This comprehensive nature of speech significantly impacts communication and is crucial for …
Dialogue UnderstandingItem-Language Model for Conversational Recommendation
Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abilities have been extended with multi-modal…
Conversational RecommendationDialogue UnderstandingLanguage ModelingLanguage Modelling+2MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions
Large language models (LLMs) have demonstrated impressive capabilities in mathematical problem solving, particularly in single turn question answering formats. However, real world scenarios often involve mathematical que…
BenchmarkingDialogue UnderstandingInstruction FollowingMath+4Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models
Deception and persuasion play a critical role in long-horizon dialogues between multiple parties, especially when the interests, goals, and motivations of the participants are not aligned. Such complex tasks pose challen…
Decision MakingDialogue UnderstandingPromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain
Biomedical language understanding benchmarks are the driving forces for artificial intelligence applications with large language model (LLM) back-ends. However, most current benchmarks: (a) are limited to English which m…
Dialogue GenerationDialogue UnderstandingKnowledge ProbingLanguage Modeling+5From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues
Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC). While considerable research has focused on discerning emotion…
Dialogue UnderstandingEmotional IntelligenceEmotion RecognitionEmotion Recognition in Conversation+1Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task
With the increasing capabilities of large language models (LLMs), these high-performance models have achieved state-of-the-art results on a wide range of natural language processing (NLP) tasks. However, the models' perf…
Data AugmentationDialogue UnderstandingSentenceslot-filling+1Self-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. In this study, we pr…
Dialogue Understanding