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    • Korhonen, Anna-Leena (4)
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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 – 6 of 6 1 Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization ... Ponti, Edoardo; Vulić, I; Glavaš, G. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 2 Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization Ponti, Edoardo; Vulić, I; Glavaš, G. - : Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, 2020 BASE Show details 3 Fully statistical neural belief tracking Mrkšić, N; Vulić, I. - : Association for Computational Linguistics, 2018. : ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 2018 BASE Show details 4 Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules ... Vulic, Ivan; Mrkšic, N; Reichart, R. - : Apollo - University of Cambridge Repository, 2017 BASE Show details 5 Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules Vulic, Ivan; Mrkšic, N; Reichart, R; Séaghdha, D; Young, Steve; Korhonen, Anna-Leena. - : Association for Computational Linguistics, 2017. : ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 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 that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks. Keyword: Dialogue state tracking; Morphologically complex languages; Semantic specialisation; Vector space models; Word embeddings URL: https://doi.org/10.17863/CAM.10176 https://www.repository.cam.ac.uk/handle/1810/264637 BASE Hide details 6 Multi-domain neural network language generation for spoken dialogue systems Wen, TH; Gašić, M; Mrkšić, N. - : Association for Computational Linguistics, 2016. : 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference, 2016 BASE Show details

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