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Potential of Dedicated Language Processing Units in Computer Voice Interaction
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In: Idaho Conference on Undergraduate Research (2017)
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An outline of type-theoretical approaches to lexical semantics
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In: ISSN: 2299-856X ; EISSN: 2299-8470 ; Journal of Language Modelling ; https://hal.archives-ouvertes.fr/hal-01802968 ; Journal of Language Modelling, Institute of Computer Science, Polish Academy of Sciences, Poland, 2017, 5 (2), pp.165-178. ⟨10.15398/jlm.v5i2.200⟩ (2017)
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Classifiers, Sorts, and Base Types in the Montagovian Generative Lexicon and Related Type Theoretical Frameworks for Lexical Compositional Semantics
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In: Modern Perspectives in Type-Theoretical Semantics ; https://hal.archives-ouvertes.fr/hal-01471256 ; Modern Perspectives in Type-Theoretical Semantics, Studies in Linguistics and Philosophy (98), pp.163-188, 2017, 978-3-319-50422-3. ⟨10.1007/978-3-319-50422-3_7⟩ (2017)
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СОСТАВ И СТРУКТУРА КОРПУСА ТЕКСТОВ RUSSIAN DECEPTION BANK, ПРЕДНАЗНАЧЕННОГО ДЛЯ РАЗРАБОТКИ МЕТОДИК ДИАГНОСТИРОВАНИЯ ЛЖИ В РЕЧИ
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Domain adaptation for statistical machine translation and neural machine translation
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Zhang, Jian. - : Dublin City University. School of Computing, 2017. : Dublin City University. ADAPT, 2017
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In: Zhang, Jian orcid:0000-0001-5659-5865 (2017) Domain adaptation for statistical machine translation and neural machine translation. PhD thesis, Dublin City University. (2017)
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Abstract:
Both Statistical Machine Translation and Neural Machine Translation (NMT) are data-dependent learning approaches to Machine Translation (MT). The prerequisite is a large volume of training data in order to generate good statistical models. However, even if large volume of training corpora are available for MT, finding training data which are similar to the specific domains is still difficult. The MT models trained using the limited specific domain data cannot have sufficient coverage on the linguistic phenomena in that domain, which makes this a very challenging task. Because word meanings, genres or topics differ between domains, using the additional data from other domains can increase the dissimilar- ities between the training and testing data, and result in reduced translation quality. Such a challenge is defined as ‘domain adaptation’ challenge in the literature. In this thesis, we investigate domain adaptation in two different scenarios, namely a domain-awareness scenario and a domain-unawareness scenario. In a domain-awareness scenario, the domain information is given explicitly in the training data. We are interested in developing domain-adaptation techniques which transfer knowledge gained from the other domains to a desired domain. In the approach proposed here probabilistic values indicating the domain-likeness features for words are estimated by the context rather than by the words themselves. We then apply those features to the combined translation models in an MT system. We empirically show that translation quality can be significantly improved compared with previous related work. We then turn our interest to the recently proposed neural network training. We describe a domain-adaptation approach which can exploit large pre-trained word vector models. We evaluate our approach on both language modelling and machine translation tasks to demonstrate its efficiency, effectiveness and flexibility in a domain-awareness scenario. xiiIn a domain-unawareness scenario, the domain information is not given explicitly in the training data. The training data is heterogeneous, e.g. originated from tens or even hundreds of different resources without well-defined domain labels. We overcome such a challenge by deriving the topic information from the training corpora using well-estimated topic modelling algorithms. In this scenario, we pay particular attention to the most recent NMT framework. We are concerning with making a better lexical choice and improving the overall translation quality. Experimentally, we show that our model can perform better lexical choice, improve the overall translation quality and reduce the number of unknown words.
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Keyword:
Artificial intelligence; Computational linguistics; Machine learning; Machine translating
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URL: http://doras.dcu.ie/21949/
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Detection of verbal multi-word expressions via conditional random fields with syntactic dependency features and semantic re-ranking
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In: Maldonado, Alfredo orcid:0000-0001-8426-5249 , Han, Lifeng orcid:0000-0002-3221-2185 , Moreau, Erwan orcid:0000-0001-7692-526X , Alsulaimani, Ashjan, Chowdhury, Koel, Vogel, Carl orcid:0000-0001-8928-8546 and Liu, Qun orcid:0000-0002-7000-1792 (2017) Detection of verbal multi-word expressions via conditional random fields with syntactic dependency features and semantic re-ranking. In: 13th Workshop on Multiword Expressions (MWE 2017), Apr 2017, Valencia, Spain. (2017)
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Incorporating visual information into neural machine translation
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Calixto, Iacer. - : Dublin City University. School of Computing, 2017. : Dublin City University. ADAPT, 2017
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In: Calixto, Iacer (2017) Incorporating visual information into neural machine translation. PhD thesis, Dublin City University. (2017)
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Dublin City University participation in the VTT track at TRECVid 2017
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In: Afli, Haithem orcid:0000-0002-7449-4707 , Hu, Feiyan orcid:0000-0001-7451-6438 , Du, Jinhua orcid:0000-0002-3267-4881 , Cosgrove, Daniel, McGuinness, Kevin orcid:0000-0003-1336-6477 , O'Connor, Noel E. orcid:0000-0002-4033-9135 , Arazo Sánchez, Eric, Zhou, Jiang orcid:0000-0002-3067-8512 and Smeaton, Alan F. orcid:0000-0003-1028-8389 (2017) Dublin City University participation in the VTT track at TRECVid 2017. In: TRECVid workshop, 13-15 Nov 2017, Gaithersburg, Md., USA. (2017)
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Machine translation of morphologically rich languages using deep neural networks
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Passban, Peyman. - : Dublin City University. School of Computing, 2017. : Dublin City University. ADAPT, 2017
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In: Passban, Peyman (2017) Machine translation of morphologically rich languages using deep neural networks. PhD thesis, Dublin City University. (2017)
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Entity linking for Tweets
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In: Basile, Pierpaolo orcid:0000-0002-0545-1105 and Caputo, Annalina orcid:0000-0002-7144-8545 (2017) Entity linking for Tweets. Encyclopedia with Semantic Computing and Robotic Intelligence, 1 (1). pp. 1-9. ISSN 2529-7376 (2017)
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How do users perceive information: analyzing user feedback while annotating textual units
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In: Arora, Piyush orcid:0000-0003-0055-345X and Jones, Gareth J.F. orcid:0000-0003-2923-8365 (2017) How do users perceive information: analyzing user feedback while annotating textual units. In: CHIIR 2017 Workshop on Supporting Complex Search Tasks, 11 Mar 2017, Oslo, Norway. (2017)
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