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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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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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Abstract:
In this work, we study different ways to enrich Machine Translation (MT) models using information obtained from images. Specifically, we propose different models to incorporate images into MT by transferring learning from pre-trained convolutional neural networks (CNN) trained for classifying images. We use these pre-trained CNNs for image feature extraction, and use two different types of visual features: global visual features, that encode an entire image into one single real-valued feature vector; and local visual features, that encode different areas of an image into separate real-valued vectors, therefore also encoding spatial information. We first study how to train embeddings that are both multilingual and multi-modal, and use global visual features and multilingual sentences for training. Second, we propose different models to incorporate global visual features into state-of-the-art Neural Machine Translation (NMT): (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to initialise the decoder hidden state. Finally, we put forward one model to incorporate local visual features into NMT: (i) a NMT model with an independent visual attention mechanism integrated into the same decoder Recurrent Neural Network (RNN) as the source-language attention mechanism. We evaluate our models on the Multi30k, a publicly available, general domain data set, and also on a proprietary data set of product listings and images built by eBay Inc., which was made available for the purpose of this research. We report state-of-the-art results on the publicly available Multi30k data set. Our best models also significantly improve on comparable phrase-based Statistical MT (PBSMT) models trained on the same data set, according to widely adopted MT metrics.
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Keyword:
Artificial intelligence; Computational linguistics; Machine learning; Machine translating
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URL: http://doras.dcu.ie/21942/
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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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