A Unified Implicit Dialog Framework for Conversational Search
We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Search applications. It aims to enable dialog interactions with domain data without replying on explicitly encoded the rules but utilizing the underlying data representation to build the components required for dialog interaction, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. A centralized knowledge representation is used to semantically ground multiple dialog modules. An associated set of tools are integrated with the framework to gather end users' input for continuous improvement of the system. The goal is to facilitate development of conversational systems by identifying the components and the data that can be adapted and reused across many end-user applications. We demonstrate our approach by creating conversational agents for several independent domains.
Code (0)
등록된 구현이 없습니다.
Tasks
Conversational SearchSimilar Papers 제목 키워드 기반
Unified Conversational Models with System-Initiated Transitions between Chit-Chat and Task-Oriented Dialogues
Spoken dialogue systems (SDSs) have been separately developed under two different categories, task-oriented and chit-chat. The former focuses on achieving functional goals and the latter aims at creating engaging social …
SentenceSpoken Dialogue SystemsDialogStudio: 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+2Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems
Sharing ideas through communication with peers is the primary mode of human interaction. Consequently, extensive research has been conducted in the area of conversational AI, leading to an increase in the availability an…
Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History
Proactive dialogue systems aim to empower chatbots with the capability of leading conversations towards specific targets, thereby enhancing user engagement and service autonomy. Existing systems typically target pre-defi…
RAGRetrievalRetrieval-augmented GenerationMConv: An Environment for Multimodal Conversational Search across Multiple Domains
Although conversational search has become a hot topic in both dialogue research and IR community, the real breakthrough has been limited by the scale and quality of datasets available. To address this fundamental obstacl…
Conversational RecommendationConversational SearchDialogue State TrackingResponse Generation