TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles
In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across diverse usage scenarios. However, the creation of high-quality annotated data for Task-Oriented Dialog (TOD) is recognized to be slow and costly. To address these challenges, we introduce Task-Oriented Automatic Dialogs (TOAD), a novel and scalable TOD dataset along with its automatic generation pipeline. The TOAD dataset simulates realistic app context interaction and provide a variety of system response style options. Two aspects of system response styles are considered, verbosity level and users' expression mirroring. We benchmark TOAD on two response generation tasks, and the results show that modeling more verbose responses or responses without user expression mirroring is more challenging.
Code (0)
등록된 구현이 없습니다.
Tasks
Response GenerationSimilar Papers 제목 키워드 기반
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversity and comprehensiveness. To tackle thes…
Conversational RecommendationDiversityFew-Shot LearningLanguage Modeling+2DialogStitch: Synthetic Deeper and Multi-Context Task-Oriented Dialogs
Real-world conversational agents must effectively handle long conversations that span multiple contexts. Such context can be interspersed with chitchat (dialog turns not directly related to the task at hand), and potenti…
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation
Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. In this work, we extend this approach to the ta…
Data Augmentationdialog state trackingDialogue State TrackingResponse Generation+2CookDial: A dataset for task-oriented dialogs grounded in procedural documents
This work presents a new dialog dataset, CookDial, that facilitates research on task-oriented dialog systems with procedural knowledge understanding. The corpus contains 260 human-to-human task-oriented dialogs in which …
Decision MakingResponse GenerationSIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations
Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal environment. Existing task-oriented dialog…
DiversityLanguage ModelingLanguage Modelling