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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
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Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation
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The Universitat d'Alacant submissions to the English-to-Kazakh news translation task at WMT 2019 ...
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The Universitat d'Alacant submissions to the English-to-Kazakh news translation task at WMT 2019 ...
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Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian ...
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Fine-grained human evaluation of neural versus phrase-based machine translation ...
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A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions ...
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Assisting non-expert speakers of under-resourced languages in assigning stems and inflectional paradigms to new word entries of morphological dictionaries
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Abstract:
This paper presents a new method with which to assist individuals with no background in linguistics to create monolingual dictionaries such as those used by the morphological analysers of many natural language processing applications. The involvement of non-expert users is especially critical for under-resourced languages which either lack or cannot afford the recruitment of a skilled workforce. Adding a word to a morphological dictionary usually requires identifying its stem along with the inflection paradigm that can be used in order to generate all the word forms of the new entry. Our method works under the assumption that the average speakers of a language can successfully answer the polar question “is x a valid form of the word w to be inserted?”, where x represents tentative alternative (inflected) forms of the new word w. The experiments show that with a small number of polar questions the correct stem and paradigm can be obtained from non-experts with high success rates. We study the impact of different heuristic and probabilistic approaches on the actual number of questions. ; This work has been partially funded by the Spanish Ministry of Science & Innovation through project TIN2009-14009-C02-01, by the Spanish Ministry of Economy & Competitiveness through Project TIN2012-32615, by the Generalitat Valenciana through grant ACIF/2010/174 from VALi+d programme, and by the European Commission through Project PIAP-GA-2012-324414 (Abu-MaTran).
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Keyword:
Enlargement of morphological dictionaries; Knowledge elicitation; Lenguajes y Sistemas Informáticos; Machine translation; Resource development for under-resourced languages
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URL: http://hdl.handle.net/10045/71353 https://doi.org/10.1007/s10579-016-9360-9
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Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 121-132 (2017) (2017)
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Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation
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RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation
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RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation
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In: Prague Bulletin of Mathematical Linguistics , Vol 106, Iss 1, Pp 193-204 (2016) (2016)
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A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora
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An open-source toolkit for integrating shallow-transfer rules into phrase-based statistical machine translation
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Choosing the correct paradigm for unknown words in rule-based machine translation systems
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The Universitat d’Alacant hybrid machine translation system for WMT 2011
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Integrating shallow-transfer rules into phrase-based statistical machine translation
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Enriching a statistical machine translation system trained on small parallel corpora with rule-based bilingual phrases
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A Widely Used Machine Translation Service and its Migration to a Free/Open-Source Solution : the Case of Softcatalà
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ScaleMT: a free/open-source framework for building scalable machine translation web services
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