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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. - : Association for Computational Linguistics, 2017. : ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 2017 BASE Show details 6 Multi-domain neural network language generation for spoken dialogue systems Wen, TH; Gašić, M; Mrkšić, N; Rojas-Barahona, LM; Su, PH; Vandyke, D; Young, Steve. - : 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 Abstract: Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains. Therefore, it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation. In this paper, we propose a procedure to train multi-domain, Recurrent Neural Network-based (RNN) language generators via multiple adaptation steps. In this procedure, a model is first trained on counterfeited data synthesised from an out-of-domain dataset, and then fine tuned on a small set of in-domain utterances with a discriminative objective function. Corpus-based evaluation results show that the proposed procedure can achieve competitive performance in terms of BLEU score and slot error rate while significantly reducing the data needed to train generators in new, unseen domains. In subjective testing, human judges confirm that the procedure greatly improves generator performance when only a small amount of data is available in the domain. ; Toshiba Research Europe Ltd. ; This is the accepted manuscript. It is currently embargoed pending publication. URL: https://www.repository.cam.ac.uk/handle/1810/256144 https://doi.org/10.17863/CAM.84 BASE Hide details

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