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Hits 21 – 37 of 37

21	Improving machine translation of English relative clauses with automatic text simplification
	Popović, Maja; Štajner, Sanja
	In: Štajner, Sanja and Popović, Maja orcid:0000-0001-8234-8745 (2018) Improving machine translation of English relative clauses with automatic text simplification. In: INLG 1st Workshop on Automatic Text Adaptation (ATA 18), 5-8 Nov 2018, Tilburg, Netherlands. (2018)
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22	NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation
	Hayes, Jer; Wan, Dadong; Barman, Utsab...
	In: Han, Jingguang, Barman, Utsab, Hayes, Jer, Du, Jinhua orcid:0000-0002-3267-4881 , Burgin, Edward and Wan, Dadong (2018) NextGen AML: distributed deep learning based language technologies to augment anti money laundering Investigation. In: 56th Annual Meeting of the Association for Computational Linguistics-System Demonstrations, 15-20 July 201, Melbourne, Australia. (2018)
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23	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)
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24	Incorporating Chinese radicals into neural machine translation: deeper than character level
	Kuang, Shaohui; Han, Lifeng
	In: Han, Lifeng orcid:0000-0002-3221-2185 and Kuang, Shaohui (2018) Incorporating Chinese radicals into neural machine translation: deeper than character level. In: 30th European Summer School in Logic, Language and Information (ESSLLI 2018), 6-17 Aug 2018, Sofia, Bulgaria. (2018)
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25	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)
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26	Incorporating Chinese radicals into neural machine translation: deeper Than character level
	Kuang, Shaohui; Han, Lifeng
	In: Han, Lifeng and Kuang, Shaohui (2018) Incorporating Chinese radicals into neural machine translation: deeper Than character level. In: 30th European Summer School in Logic, Language and Information (ESSLLI 2018), 6-17 Aug 2018, Sofia, Bulgaria. (2018)
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27	Findings of the 2018 Conference on Machine Translation (WMT18)
	Federmann, Christian; Bojar, Ondřej; Graham, Yvette...
	In: Bojar, Ondřej orcid:0000-0002-0606-0050 , Federmann, Christian, Fishel, Mark, Graham, Yvette and Haddow, Barry (2018) Findings of the 2018 Conference on Machine Translation (WMT18). In: Third Conference on Machine Translation, 31 Oct- 1 Nov 2018, Brussels, Belgium. (2018)
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28	Apply Chinese radicals Into neural machine translation/ deeper than character level
	Han, Lifeng
	In: Han, Lifeng orcid:0000-0002-3221-2185 (2018) Apply Chinese radicals Into neural machine translation/ deeper than character level. In: LPRC 2018: Limerick Postgraduate Research Conference, 24 May 2018, Limerick, Ireland. (2018)
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29	Attaining the unattainable? Reassessing claims of human parity in neural machine translation
	Toral, Antonio; Hu, Ke; Way, Andy...
	In: Toral, Antonio orcid:0000-0003-2357-2960 , Castilho, Sheila orcid:0000-0002-8416-6555 , Hu, Ke and Way, Andy orcid:0000-0001-5736-5930 (2018) Attaining the unattainable? Reassessing claims of human parity in neural machine translation. In: Third Conference on Machine Translation (WMT), 31 Oct- 1 Nov 2018, Brussels, Belgium. ISBN 978-1-948087-81-0 (2018)
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30	Multi-level structured self-attentions for distantly supervised relation extraction
	Wan, Dadong; Du, Jinhua; Way, Andy...
	In: Du, Jinhua orcid:0000-0002-3267-4881 , Han, Jingguang, Way, Andy orcid:0000-0001-5736-5930 and Wan, Dadong (2018) Multi-level structured self-attentions for distantly supervised relation extraction. In: 2018 Conference on Empirical Methods in Natural Language Processing, 31 Oct - 4 Nov 2018, Brussels, Belgium. (2018)
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31	Findings of the 2018 conference on machine translation (WMT18)
	Federmann, Christian; Koehn, Philipp; Monz, Christof...
	In: Bojar, Ondřej orcid:0000-0002-0606-0050 , Federmann, Christian, Fishel, Mark, Graham, Yvette, Haddow, Barry, Huck, Matthias, Koehn, Philipp and Monz, Christof (2018) Findings of the 2018 conference on machine translation (WMT18). In: Third Conference on Machine Translation, Volume 2: Shared Task Papers, 31 Oct - 1 Nov 2018, Brussels, Belgium. (2018)
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32	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)
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33	Machine Translation of Arabic Dialects
	Salloum, Wael Sameer. - 2018
	Abstract: This thesis discusses different approaches to machine translation (MT) from Dialectal Arabic (DA) to English. These approaches handle the varying stages of Arabic dialects in terms of types of available resources and amounts of training data. The overall theme of this work revolves around building dialectal resources and MT systems or enriching existing ones using the currently available resources (dialectal or standard) in order to quickly and cheaply scale to more dialects without the need to spend years and millions of dollars to create such resources for every dialect. Unlike Modern Standard Arabic (MSA), DA-English parallel corpora is scarcely available for few dialects only. Dialects differ from each other and from MSA in orthography, morphology, phonology, and to some lesser degree syntax. This means that combining all available parallel data, from dialects and MSA, to train DA-to-English statistical machine translation (SMT) systems might not provide the desired results. Similarly, translating dialectal sentences with an SMT system trained on that dialect only is also challenging due to different factors that affect the sentence word choices against that of the SMT training data. Such factors include the level of dialectness (e.g., code switching to MSA versus dialectal training data), topic (sports versus politics), genre (tweets versus newspaper), script (Arabizi versus Arabic), and timespan of test against training. The work we present utilizes any available Arabic resource such as a preprocessing tool or a parallel corpus, whether MSA or DA, to improve DA-to-English translation and expand to more dialects and sub-dialects. The majority of Arabic dialects have no parallel data to English or to any other foreign language. They also have no preprocessing tools such as normalizers, morphological analyzers, or tokenizers. For such dialects, we present an MSA-pivoting approach where DA sentences are translated to MSA first, then the MSA output is translated to English using the wealth of MSA-English parallel data. Since there is virtually no DA-MSA parallel data to train an SMT system, we build a rule-based DA-to-MSA MT system, ELISSA, that uses morpho-syntactic translation rules along with dialect identification and language modeling components. We also present a rule-based approach to quickly and cheaply build a dialectal morphological analyzer, ADAM, which provides ELISSA with dialectal word analyses. Other Arabic dialects have a relatively small-sized DA-English parallel data amounting to a few million words on the DA side. Some of these dialects have dialect-dependent preprocessing tools that can be used to prepare the DA data for SMT systems. We present techniques to generate synthetic parallel data from the available DA-English and MSA- English data. We use this synthetic data to build statistical and hybrid versions of ELISSA as well as improve our rule-based ELISSA-based MSA-pivoting approach. We evaluate our best MSA-pivoting MT pipeline against three direct SMT baselines trained on these three parallel corpora: DA-English data only, MSA-English data only, and the combination of DA-English and MSA-English data. Furthermore, we leverage the use of these four MT systems (the three baselines along with our MSA-pivoting system) in two system combination approaches that benefit from their strengths while avoiding their weaknesses. Finally, we propose an approach to model dialects from monolingual data and limited DA-English parallel data without the need for any language-dependent preprocessing tools. We learn DA preprocessing rules using word embedding and expectation maximization. We test this approach by building a morphological segmentation system and we evaluate its performance on MT against the state-of-the-art dialectal tokenization tool.
	Keyword: Arabic language--Dialects; Arabic language--Machine translating; Artificial intelligence; Computer science
	URL: https://doi.org/10.7916/D8Q25H44
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34	La traducció automàtica amb postedició en una UE multilingüe: el cas del català
	Santanach Sabatés, Laia. - : Universitat Oberta de Catalunya, 2018
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35	Machine Translation of Arabic Dialects ...
	Salloum, Wael Sameer. - : Columbia University, 2018
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36	Machine-translation inspired reordering as preprocessing for cross-lingual sentiment analysis
	Ramírez Atrio, Alejandro. - 2018
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37	La traducció automàtica amb postedició en una UE multilingüe: el cas del català
	Santanach Sabatés, Laia. - : Universitat Oberta de Catalunya, 2018
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