Results 1 to 10 of about 70,068 (263)
Discourse Structure in Machine Translation Evaluation [PDF]
Computational Linguistics, 2017 In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance ...
Shafiq Joty +3 more
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Evaluating the Impact of Integrating Similar Translations into Neural Machine Translation [PDF]
Information, 2022 Previous research has shown that simple methods of augmenting machine translation training data and input sentences with translations of similar sentences (or fuzzy matches), retrieved from a translation memory or bilingual corpus, lead to considerable ...
Arda Tezcan, Bram Bulté
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Automatic evaluation of the quality of machine translation of a scientific text: the results of a five-year-long experiment [PDF]
E3S Web of Conferences, 2021 We report on various approaches to automatic evaluation of machine translation quality and describe three widely used methods. These methods, i.e. methods based on string matching and n-gram models, make it possible to compare the quality of machine ...
Ulitkin Ilya +3 more
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A review of existing Machine Translation Approaches, their Challenges and Evaluation Metrics
Pakistan Journal of Engineering Technology & Science, 2023 Machine translation is the process of translating a natural language into another. The primary goal of machine translation is to bridge the linguistic gap between languages.
Naseer Ahmed
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Evaluating Neural Machine Translation Using Error Analysis In English -Arabic Texts [PDF]
مجلة کلية الآداب. جامعة أسوان, 2019 The aim of this study was to evaluate the output of Neural Machine Translation of translating texts from English into Arabic using error analysis. Google Translate was taken as an example as the leading neural machine translations.
فهد بن سعد السهلي
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Comparative Legilinguistics, 2019 With the advent of the neural paradigm, machine translation has made another leap in quality. As a result, its use by trainee translators has increased considerably, which cannot be disregarded in translation pedagogy.
Wiesmann Eva
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(Meta-) evaluation of machine translation [PDF]
Proceedings of the Second Workshop on Statistical Machine Translation - StatMT '07, 2007 This paper evaluates the translation quality of machine translation systems for 8 language pairs: translating French, German, Spanish, and Czech to English and back. We carried out an extensive human evaluation which allowed us not only to rank the different MT systems, but also to perform higher-level analysis of the evaluation process.
Callison-Burch, Chris +4 more
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On The Evaluation of Machine Translation Systems Trained With Back-Translation [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 ACL ...
Sergey Edunov +3 more
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Difficulty-Aware Machine Translation Evaluation [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), 2021 The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation. Current MT evaluation pays the same attention to each sentence component, while the questions of real-world examinations (e.g., university examinations) have different difficulties and weightings.
Runzhe Zhan +3 more
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Multi-Hypothesis Machine Translation Evaluation [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 Reliably evaluating Machine Translation (MT) through automated metrics is a long-standing problem. One of the main challenges is the fact that multiple outputs can be equally valid. Attempts to minimise this issue include metrics that relax the matching of MT output and reference strings, and the use of multiple references. The latter has been shown to
Fomicheva, M., Specia, L., Guzmán, F.
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