Neural Belief Tracker: Data-Driven Dialogue State Tracking
Nikola Mrkšić,Diarmuid Ó Séaghdha,Tsung-Hsien Wen,Blaise Thomson,Steve Young +4 more
- Vol. 1, pp 1777-1788
TLDR
This work proposes a novel Neural Belief Tracking (NBT) framework which overcomes past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.Abstract:
One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user’s goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users’ language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.read more
Citations
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Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning
TL;DR: The experiment results show that the proposed method can successfully control several state-of-the-art (SOTA) dialogue models without accessing their parameters and demonstrates the strong ability to quickly adapt to an unseen task in fewer steps than the baseline model.
Journal ArticleDOI
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking
TL;DR: This paper proposes a multi-domain and multi-lingual dialogue state 008 tracker in a neural reading comprehension approach and shows its competitive transferability by zero-shot domain-adaptation experiments on MultiWOZ 2.1.
Proceedings ArticleDOI
WeaSuL: Weakly Supervised Dialogue Policy Learning: Reward Estimation for Multi-turn Dialogue
TL;DR: In this article, an agent uses dynamic blocking to generate ranked diverse responses and exploration-exploitation to select among the Top-K responses, and each simulated state-action pair is evaluated (works as a weak annotation) with three quality modules: Semantic Relevant, Semantic Coherence and Consistent Flow.
Peer Review
« Est-ce que tu me suis ? » : une revue du suivi de l’état du dialogue (“Do you follow me ?" : a review of dialogue state tracking )
TL;DR: In this article , a système de dialogue orienté tâche doit suivre les besoins de l'utilisateur à chaque étape selon l'historique de la conversation.
Journal ArticleDOI
A Chit-Chats Enhanced Task-Oriented Dialogue Corpora for Fuse-Motive Conversation Systems
TL;DR: This work releases a multi-turn dialogues dataset called Chinese ChatEnhanced-Task (CCET) and proposes a line of fuse-motive dialogues formalization approach, along with several evaluation metrics for TOD sessions that are integrated by CC utterances.
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