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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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| 7 |
A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions ...
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| 8 |
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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| 9 |
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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| 11 |
RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation
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Abstract:
This paper presents ruLearn, an open-source toolkit for the automatic inference of rules for shallow-transfer machine translation from scarce parallel corpora and morphological dictionaries. ruLearn will make rule-based machine translation a very appealing alternative for under-resourced language pairs because it avoids the need for human experts to handcraft transfer rules and requires, in contrast to statistical machine translation, a small amount of parallel corpora (a few hundred parallel sentences proved to be sufficient). The inference algorithm implemented by ruLearn has been recently published by the same authors in Computer Speech & Language (volume 32). It is able to produce rules whose translation quality is similar to that obtained by using hand-crafted rules. ruLearn generates rules that are ready for their use in the Apertium platform, although they can be easily adapted to other platforms. When the rules produced by ruLearn are used together with a hybridisation strategy for integrating linguistic resources from shallow-transfer rule-based machine translation into phrase-based statistical machine translation (published by the same authors in Journal of Artificial Intelligence Research, volume 55), they help to mitigate data sparseness. This paper also shows how to use ruLearn and describes its implementation. ; Research funded by the Spanish Ministry of Economy and Competitiveness through projects TIN2009-14009-C02-01 and TIN2012-32615, by Generalitat Valenciana through grant ACIF/2010/174, and by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (Abu-MaTran).
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Keyword:
Automatic inference; Lenguajes y Sistemas Informáticos; Machine translation; ruLearn; Shallow-transfer rules
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URL: http://hdl.handle.net/10045/60039
https://doi.org/10.1515/pralin-2016-0018
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| 12 |
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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| 14 |
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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| 19 |
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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