Context-Aware Language Modeling for Goal-Oriented Dialogue Systems
Goal-oriented dialogue systems has long faced the trade-off between fluent language generation and task-specific control. While supervised learning with large language models are capable of producing realistic responses, how to steer such responses towards completing a specific task without sacrificing language quality remains an open question. In this work, by viewing a goal-oriented dialogue system as a reinforcement learning (RL) problem, we turn a supervised language model into a dynamics model and a behavioral cloning policy in a partially observable Markov decision process. This view allows RL techniques such as task relabeling and goal-conditioned policy to be naturally adopted as a form of data augmentation and task-specific fintuning of language models. We evaluate our method, Context-Aware Language Models (\method), on a practical flight-booking task using AirDialogue. Empirically, \method outperforms the previous state-of-the-art method by more than 10\% in terms of task success, achieving human-level task performance on this dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationGoal-Oriented Dialogue SystemsLanguage ModelingLanguage ModellingOpen-Ended Question AnsweringReinforcement Learning (RL)Text GenerationSimilar Papers 제목 키워드 기반
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems
Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control. While supervised learning with large language models is capable of producing realistic text, how to steer such…
Goal-Oriented Dialogue SystemsLanguage ModelingLanguage ModellingOpen-Ended Question Answering+1Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems
Domain specific goal-oriented dialogue systems typically require modeling three types of inputs, viz., (i) the knowledge-base associated with the domain, (ii) the history of the conversation, which is a sequence of utter…
Document DatingGoal-Oriented Dialogue SystemsMachine TranslationSemantic Role LabelingGrounding Agent Memory in Contextual Intent
Deploying large language models in long-horizon, goal-oriented interactions remains challenging because similar entities and facts recur under different latent goals and constraints, causing memory systems to retrieve co…
Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog
Creating an intelligent conversational system that understands vision and language is one of the ultimate goals in Artificial Intelligence (AI)~\cite{winograd1972understanding}. Extensive research has focused on vision-t…
Hierarchical Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+2Large Language Models as User-Agents for Evaluating Task-Oriented-Dialogue Systems
Traditionally, offline datasets have been used to evaluate task-oriented dialogue (TOD) models. These datasets lack context awareness, making them suboptimal benchmarks for conversational systems. In contrast, user-agent…
DiversityTask-Oriented Dialogue Systems