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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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Multi-domain Dialog State Tracking using Recurrent Neural Networks ...
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Abstract:
Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, well-defined domain in mind. This paper shows that dialog data drawn from different dialog domains can be used to train a general belief tracking model which can operate across all of these domains, exhibiting superior performance to each of the domain-specific models. We propose a training procedure which uses out-of-domain data to initialise belief tracking models for entirely new domains. This procedure leads to improvements in belief tracking performance regardless of the amount of in-domain data available for training the model. ... : Accepted as a short paper in the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/1506.07190 https://dx.doi.org/10.48550/arxiv.1506.07190
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Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking ...
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