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Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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Data-Driven Language Understanding for Spoken Dialogue Systems ...
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Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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Post-specialisation: Retrofitting vectors of words unseen in lexical resources
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Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
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Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ...
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Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules ...
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Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation ...
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Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ...
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Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
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Mrkšić, Nikola; Vulić, Ivan; Ó Séaghdha, Diarmuid. - : Association for Computational Linguistics, 2017. : https://www.transacl.org/ojs/index.php/tacl/article/view/1171, 2017. : Transactions of the Association for Computational Linguistics (TACL), 2017
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Neural Belief Tracker: Data-Driven Dialogue State Tracking ...
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Research data supporting "On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems"
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Abstract:
This repository contains the data presented in the paper "On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems" in ACL 2016. Two separate datasets as described in section 4 of the paper are presented: 1. DialogueEmbedding/ It contains the [train|valid|test] data for the unsupervised dialogue embedding creation, each with *.[feature|reward|turn|subjsuc]. Note that *.turn includes the lines to be read for each dialogue in *.[feature|reward|subjsuc], and *.subjsuc is the user's subjective rating. The feature size is 74. 2. DialoguePolicy/ It includes four contrasting systems with different reward models: [GP|RNN|ObjSubj|Subj]. Inside each system directory is the data obtained in interaction with Amazon Mechanical Turk users while training three policies with same config: policy_[1|2|3]. and a .csv for the evaluation result along with the trainig process. In each policy_[1|2|3]/ there is a list of calls with a time stamp in the name which contains session.xml file for dialogue log and feedback.xml file for user feedback ; This research data supports "On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems" which has been published in "Proceedings of Association for Computational Linguistics (ACL)". ; This work was supported by the EPSRC [grant number Cambridge Trust].
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Keyword:
deep learning; Gaussian process; reinforcement learning; reward modelling; spoken dialogue systems
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URL: https://www.repository.cam.ac.uk/handle/1810/256020
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Multi-domain Dialog State Tracking using Recurrent Neural Networks ...
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Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking ...
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