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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 – 5 of 5 1 Deep interactive text prediction and quality estimation in translation interfaces Hokamp, Christopher M.. - : Dublin City University. School of Computing, 2018 In: Hokamp, Christopher M. (2018) Deep interactive text prediction and quality estimation in translation interfaces. PhD thesis, Dublin City University. (2018) BASE Show details 2 Translating pro-drop languages with reconstruction models Tu, Zhaopeng; Shi, Shuming; Graham, Yvette; Liu, Qun; Wang, Longyue; Zhang, Tong In: Wang, Longyue orcid:0000-0002-9062-6183 , Tu, Zhaopeng, Shi, Shuming, Zhang, Tong, Graham, Yvette and Liu, Qun orcid:0000-0002-7000-1792 (2018) Translating pro-drop languages with reconstruction models. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2–7 Feb 2018, New Orleans, LA, USA. ISBN 978-1-57735-800-8 (2018) Abstract: Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to significant challenges with respect to the production of complete translations. To date, very little attention has been paid to the dropped pronoun (DP) problem within neural machine translation (NMT). In this work, we propose a novel reconstruction-based approach to alleviating DP translation problems for NMT models. Firstly, DPs within all source sentences are automatically annotated with parallel information extracted from the bilingual training corpus. Next, the annotated source sentence is reconstructed from hidden representations in the NMT model. With auxiliary training objectives, in terms of reconstruction scores, the parameters associated with the NMT model are guided to produce enhanced hidden representations that are encouraged as much as possible to embed annotated DP information. Experimental results on both Chinese–English and Japanese–English dialogue translation tasks show that the proposed approach significantly and consistently improves translation performance over a strong NMT baseline, which is directly built on the training data annotated with DPs. Keyword: Machine translating URL: http://doras.dcu.ie/23375/ BASE Hide details 3 Improving character-based decoding using target-side morphological information for neural machine translation Way, Andy; Liu, Qun; Passban, Peyman In: Passban, Peyman, Liu, Qun orcid:0000-0002-7000-1792 and Way, Andy orcid:0000-0001-5736-5930 (2018) Improving character-based decoding using target-side morphological information for neural machine translation. In: 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. (NAACL 2018), 1-6 June 2018, New Orleans, LA, USA. (2018) BASE Show details 4 Tailoring neural architectures for translating from morphologically rich languages Liu, Qun; Way, Andy; Passban, Peyman In: Passban, Peyman, Way, Andy orcid:0000-0001-5736-5930 and Liu, Qun orcid:0000-0002-7000-1792 (2018) Tailoring neural architectures for translating from morphologically rich languages. In: 27th International Conference on Computational Linguistics, 20-26 Aug 2018, Santa Fe, New Mexico, USA. (2018) BASE Show details 5 Multimodal neural machine translation for low-resource language pairs using synthetic data Hasanuzzaman, Mohammed; Liu, Qun; Dutta Chowdhury, Koel In: Dutta Chowdhury, Koel, Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Liu, Qun orcid:0000-0002-7000-1792 (2018) Multimodal neural machine translation for low-resource language pairs using synthetic data. In: Workshop on Deep Learning Approaches for Low-Resource NLP, 19 July 2018, Melbourne, Australia. (2018) BASE Show details

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