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Translation quality assessment: a brief survey on manual and automatic methods
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In: Han, Lifeng orcid:0000-0002-3221-2185 , Jones, Gareth J.F. orcid:0000-0003-2923-8365 and Smeaton, Alan F. orcid:0000-0003-1028-8389 (2021) Translation quality assessment: a brief survey on manual and automatic methods. In: MoTra21: Workshop on Modelling Translation: Translatology in the Digital Age, 31 May- 2 Jun 2021, Rejkjavik, Iceland (Online). (In Press) (2021)
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Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement
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In: Alekseeva, Alexandra orcid:0000-0002-7990-4592 , Gladkoff, Serge, Sorokina, Irina and Han, Lifeng orcid:0000-0002-3221-2185 (2021) Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement. In: Metrics 2021: Workshop on Informetric and Scientometric Research (SIG-MET), 23-24 Oct 2021, Online. (2021)
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Meta-evaluation of machine translation evaluation methods
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In: Han, Lifeng orcid:0000-0002-3221-2185 (2021) Meta-evaluation of machine translation evaluation methods. In: Workshop on Informetric and Scientometric Research (SIG-MET), 23-24 Oct 2021, Online. (2021)
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Learning Approximate and Exact Numeral Systems via Reinforcement Learning ...
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Mind the Gap: Language Data, Their Producers, and the Scientific Process (Crazy New Idea) ...
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Weber, Tobias. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
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Arabic Language and Computers. Application of Computational Linguistics to serve the Arabic Language
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In: ALTRALANG Journal; Vol 3 No 01 (2021): ALTRALANG Journal Volume: 03 Issue: 01 / July 2021; 138-145 ; 2710-8619 ; 2710-7922 ; 10.52919/altralang.v3i01 (2021)
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Google Assistant's Interpreter Mode: is it really an interpreter?
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MultiMWE: building a multi-lingual multi-word expression (MWE) parallel corpora
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In: Han, Lifeng orcid:0000-0002-3221-2185 , Jones, Gareth J.F. orcid:0000-0003-2923-8365 and Smeaton, Alan F. orcid:0000-0003-1028-8389 (2020) MultiMWE: building a multi-lingual multi-word expression (MWE) parallel corpora. In: 12th International Conference on Language Resources and Evaluation (LREC), 11-16 May, 2020, Marseille, France. (Virtual). (2020)
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MultiMWE: building a multi-lingual multi-word expression (MWE) Pparallel corpora
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In: Han, Lifeng, Gareth, Jones orcid:0000-0003-2923-8365 and Alan, Smeaton orcid:0000-0003-1028-8389 (2020) MultiMWE: building a multi-lingual multi-word expression (MWE) Pparallel corpora. In: International Conference on Language Resources and Evaluation (LREC), 11-16 May, 2020, Marseille, France. (2020)
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La implantación de una herramienta de gestión de proyectos de traducción en el marco de una PYME de servicios lingüísticos: el caso de PLUNET en AltaLingua
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Automated creation of domain-specific bilingual corpora for machine translation, focusing on dissimilar language pairs
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La implantación de una herramienta de gestión de proyectos de traducción en el marco de una PYME de servicios lingüísticos: el caso de PLUNET en AltaLingua
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Comparative Quality Estimation for Machine Translation. An Application of Artificial Intelligence on Language Technology using Machine Learning of Human Preferences ...
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Exploration textométrique d’un corpus comparable bilingue ... : Le corpus LSF ...
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Comparative Quality Estimation for Machine Translation. An Application of Artificial Intelligence on Language Technology using Machine Learning of Human Preferences
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COMPARISON OF AUTOMATIC SYSTEMS OF TERMS’ EXTRACTION
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In: Новітня лінгвістика; № 4 (2019): ; 29-33 ; Advanced Linguistics; № 4 (2019): Advanced Linguistics; 29-33 ; 2663-6646 ; 2617-5339 (2019)
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Incorporating Chinese radicals into neural machine translation: deeper than character level
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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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Incorporating Chinese radicals into neural machine translation: deeper Than character level
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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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Apply Chinese radicals Into neural machine translation/ deeper than character level
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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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Abstract:
Conference Presentation: In neural machine translation (NMT), researchers face the challenge of un-seen (or out-of-vocabulary OOV) words translation. To solve this, some researchers propose the splitting of western languages such as English and German into sub-words or compounds. In this paper, we try to address this OOV issue and improve the NMT adequacy with a harder language Chinese whose characters are even more sophisticated in composition. We integrate the Chinese radicals into the NMT model with different settings to address the unseen words challenge in Chinese to English translation. On the other hand, this also can be considered as semantic part of the MT system since the Chinese radicals usually carry the essential meaning of the words they are constructed in. Meaningful radicals and new characters can be integrated into the NMT systems with our models. We use an attention-based NMT system as a strong baseline system. The experiments on standard Chinese-to-English NIST translation shared task data 2006 and 2008 show that our designed models outperform the baseline model in a wide range of state-of-the-art evaluation metrics including LEPOR, BEER, and CharacTER, in addition to the traditional BLEU and NIST scores, especially on the adequacy-level translation. We also have some interesting findings from the results of our various experiment settings about the performance of words and characters in Chinese NMT, which is different with other languages. For instance, the full character level NMT may perform very well or the state of the art in some other languages as researchers demonstrated recently, however, in the Chinese NMT model, word boundary knowledge is important for the model learning.
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Keyword:
Artificial intelligence; Chinese Language Processing; Computational linguistics; Computer software; Information technology; Linguistic Aware MT; Machine learning; Machine translating; Mathematical models; Neural Machine Translation; Neural Models; Translation Quality Evaluation
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URL: http://doras.dcu.ie/26279/
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