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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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Abstract:
This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established Multidimensional Quality Metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant. We conduct a case study for English-to-Croatian, a language direction that involves translating into a morphologically rich language, for which we compare three MT systems belonging to different paradigms: pure phrase-based, factored phrase-based and neural. First, we design an MQM-compliant error taxonomy tailored to the relevant linguistic phenomena of Slavic languages, which made the annotation process feasible and accurate. Errors in MT outputs were then annotated by two annotators following this taxonomy. Subsequently, we carried out a statistical analysis which showed that the best-performing system (neural) ... : 22 pages, 2 figures, 9 tables, 1 equation. This is a post-peer-review, pre-copyedit version of an article published in Machine Translation Journal. The final authenticated version will be available online at the journal page. arXiv admin note: substantial text overlap with arXiv:1706.04389 ...
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Keyword:
68T50; Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1802.01451
https://dx.doi.org/10.48550/arxiv.1802.01451
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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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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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