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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Proceedings ArticleDOI
A Network-based End-to-End Trainable Task-oriented Dialogue System
Tsung-Hsien Wen,David Vandyke,Nikola Mrkšić,Milica Gasic,Lina Maria Rojas-Barahona,Pei-Hao Su,Stefan Ultes,Steve Young +7 more
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TL;DR: The Multi-Domain Wizard-of-Oz dataset (MultiWOZ) as discussed by the authors is a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.
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Proceedings ArticleDOI
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Paweł Budzianowski,Tsung-Hsien Wen,Bo-Hsiang Tseng,Iñigo Casanueva,Stefan Ultes,Osman Ramadan,Milica Gasic +6 more
TL;DR: The Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics is introduced, at a size of 10k dialogues, at least one order of magnitude larger than all previous annotated task-oriented corpora.
References
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Proceedings ArticleDOI
Convolutional Neural Networks for Sentence Classification
TL;DR: The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.
Proceedings Article
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot,Yoshua Bengio +1 more
TL;DR: The objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future.
Posted Content
Convolutional Neural Networks for Sentence Classification
TL;DR: In this article, CNNs are trained on top of pre-trained word vectors for sentence-level classification tasks and a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks.
Journal Article
Natural Language Processing (Almost) from Scratch
TL;DR: A unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling is proposed.
Proceedings ArticleDOI
A Convolutional Neural Network for Modelling Sentences
TL;DR: A convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) is described that is adopted for the semantic modelling of sentences and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations.