End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager
Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance. Conventional approaches aggregate separate models of natural language understanding (NLU) and system action prediction (SAP) as a pipeline that is sensitive to noisy outputs of error-prone NLU. To address the issues, we propose an end-to-end deep recurrent neural network with limited contextual dialogue memory by jointly training NLU and SAP on DSTC4 multi-domain human-human dialogues. Experiments show that our proposed model significantly outperforms the state-of-the-art pipeline models for both NLU and SAP, which indicates that our joint model is capable of mitigating the affects of noisy NLU outputs, and NLU model can be refined by error flows backpropagating from the extra supervised signals of system actions.
Code (1)
Tasks
Natural Language UnderstandingSimilar Papers 제목 키워드 기반
Taming the Beast: Learning to Control Neural Conversational Models
This thesis investigates the controllability of deep learning-based, end-to-end, generative dialogue systems in both task-oriented and chit-chat scenarios. In particular, we study the different aspects of controlling gen…
Dialogue State TrackingLanguage ModellingNatural Language UnderstandingTask-Oriented Dialogue Systems+1Emora STDM: A Versatile Framework for Innovative Dialogue System Development
This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborativ…
Dialogue ManagementManagementAction State Update Approach to Dialogue Management
Utterance interpretation is one of the main functions of a dialogue manager, which is the key component of a dialogue system. We propose the action state update approach (ASU) for utterance interpretation, featuring a st…
Active LearningDialogue ManagementManagementNatural Language UnderstandingJoint On-line Learning of a Zero-shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager
Despite many recent advances for the design of dialogue systems, a true bottleneck remains the acquisition of data required to train its components. Unlike many other language processing applications, dialogue systems re…
Dialogue ManagementManagementReinforcement Learningspeech-recognition+2Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems
Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented chatbots, help users achieve a predefined goal (e.g. book a movie ticket) within a closed domain. A first step is to understand the user's goal by …
Deep Reinforcement LearningGoal-Oriented Dialogue SystemsNatural Language UnderstandingReinforcement Learning+2