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1
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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2
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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3
Fully Statistical Neural Belief Tracking ...
Mrkšić, Nikola; Vulić, Ivan. - : arXiv, 2018
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4
Data-Driven Language Understanding for Spoken Dialogue Systems ...
Mrkšić, Nikola. - : Apollo - University of Cambridge Repository, 2018
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5
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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6
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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7
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
Vulic, Ivan; Glavaš, Goran; Mrkšić, Nikola. - : Apollo - University of Cambridge Repository, 2018
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8
Post-specialisation: Retrofitting vectors of words unseen in lexical resources
Mrkšić, Nikola; Glavaš, Goran; Korhonen, Anna. - : Association for Computational Linguistics, 2018
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9
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
Ponti, Edoardo Maria; Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018
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10
Specialising Word Vectors for Lexical Entailment ...
Vulić, Ivan; Mrkšić, Nikola. - : arXiv, 2017
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11
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ...
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12
Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules ...
Abstract: Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that 'inexpensive' is a rephrasing for 'expensive' or may not associate 'acquire' with 'acquires'. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show ... : ACL 2017 (Long paper) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1706.00377
https://arxiv.org/abs/1706.00377
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13
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation ...
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14
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ...
Mrkšić, Nikola; Vulić, Ivan; Ó Séaghdha, Diarmuid. - : Apollo - University of Cambridge Repository, 2017
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15
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
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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16
Counter-fitting Word Vectors to Linguistic Constraints ...
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17
Neural Belief Tracker: Data-Driven Dialogue State Tracking ...
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18
Research data supporting "On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems"
Su, Pei-Hao; Gasic, Milica; Mrksic, Nikola. - : University of Cambridge, 2016
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19
Multi-domain Dialog State Tracking using Recurrent Neural Networks ...
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20
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking ...
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