Integrated Learning of Dialog Strategies and Semantic Parsing
Natural language understanding and dialog management are two integral components of interactive dialog systems. Previous research has used machine learning techniques to individually optimize these components, with different forms of direct and indirect supervision. We present an approach to integrate the learning of both a dialog strategy using reinforcement learning, and a semantic parser for robust natural language understanding, using only natural dialog interaction for supervision. Experimental results on a simulated task of robot instruction demonstrate that joint learning of both components improves dialog performance over learning either of these components alone.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningManagementNatural Language Understandingreinforcement-learningReinforcement LearningReinforcement Learning (RL)Semantic ParsingSimilar Papers 제목 키워드 기반
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking
Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time-consuming and costly. However, DST extends beyond simple slot-filling and requires ef…
Dialogue State TrackingIn-Context LearningSemantic Parsingslot-filling+1Semantic Parsing for Task Oriented Dialog using Hierarchical Representations
Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots. Previous work on task oriented intent and slot-filling work has been restricted to one intent per quer…
Constituency ParsingSemantic Parsingslot-fillingSlot FillingConversational Semantic Parsing for Dialog State Tracking
We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can i…
Decoderdialog state trackingSemantic ParsingHow Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing
Building dialogue interfaces for real-world scenarios often entails training semantic parsers starting from zero examples. How can we build datasets that better capture the variety of ways users might phrase their querie…
Semantic ParsingLexicon-injected Semantic Parsing for Task-Oriented Dialog
Recently, semantic parsing using hierarchical representations for dialog systems has captured substantial attention. Task-Oriented Parse (TOP), a tree representation with intents and slots as labels of nested tree nodes,…
Semantic Parsing