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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 12 of 12 1 Multilingual and Cross-Lingual Intent Detection from Spoken Data ... Gerz, Daniela; Su, Pei-Hao; Kusztos, Razvan. - : arXiv, 2021 BASE Show details 2 Fully Statistical Neural Belief Tracking ... Mrkšić, Nikola; Vulić, Ivan. - : arXiv, 2018 BASE Show details 3 Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ... Vulić, Ivan; Glavaš, Goran; Mrkšić, Nikola. - : arXiv, 2018 BASE Show details 4 Specialising Word Vectors for Lexical Entailment ... Vulić, Ivan; Mrkšić, Nikola. - : arXiv, 2017 BASE Show details 5 Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ... Mrkšić, Nikola; Vulić, Ivan; Séaghdha, Diarmuid Ó. - : arXiv, 2017 BASE Show details 6 Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules ... Vulić, Ivan; Mrkšić, Nikola; Reichart, Roi; Séaghdha, Diarmuid Ó; Young, Steve; Korhonen, Anna. - : arXiv, 2017 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 BASE Hide details 7 Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation ... Vulić, Ivan; Mrkšić, Nikola; Korhonen, Anna. - : arXiv, 2017 BASE Show details 8 Counter-fitting Word Vectors to Linguistic Constraints ... Mrkšić, Nikola; Séaghdha, Diarmuid Ó; Thomson, Blaise. - : arXiv, 2016 BASE Show details 9 Neural Belief Tracker: Data-Driven Dialogue State Tracking ... Mrkšić, Nikola; Séaghdha, Diarmuid Ó; Wen, Tsung-Hsien. - : arXiv, 2016 BASE Show details 10 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 BASE Show details 11 Multi-domain Dialog State Tracking using Recurrent Neural Networks ... Mrkšić, Nikola; Séaghdha, Diarmuid Ó; Thomson, Blaise. - : arXiv, 2015 BASE Show details 12 Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking ... Wen, Tsung-Hsien; Gasic, Milica; Kim, Dongho. - : arXiv, 2015 BASE Show details

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