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Sebastian Tur and Maria Saiyak Kaluk Biographical Information
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Domestic Ubimus
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In: EISSN: 2409-9708 ; EAI Endorsed Transactions on Creative Technologies ; https://hal-hprints.archives-ouvertes.fr/hprints-03602695 ; EAI Endorsed Transactions on Creative Technologies, EAI - European Alliance for Innovation, 2022, ⟨10.4108/eai.22-2-2022.173493⟩ (2022)
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Metropolenzeichen: Atlas zur visuellen Mehrsprachigkeit der Metropole Ruhr ...
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Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache ...
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Fairlex: A multilingual benchmark for evaluating fairness in legal text processing ...
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Fairlex: A multilingual benchmark for evaluating fairness in legal text processing ...
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Eine Disziplin stellt um: Open-Access-Transformation in der Linguistik ...
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Eine Disziplin stellt um: Open-Access-Transformation in der Linguistik ...
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XTREME-S: Evaluating Cross-lingual Speech Representations ...
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FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing ...
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Technology Mapping Using WebAI: The Case of 3D Printing ...
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One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia ...
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The domestic sources of sub-state foreign policymaking: determinants of subnational development cooperation across European regions ...
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The domestic sources of sub-state foreign policymaking: determinants of subnational development cooperation across European regions ...
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49 - A test-suite for the evaluation of Portuguese-English machine translations ...
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Abstract:
Read full paper: https://link.springer.com/chapter/10.1007/978-3-030-98305-5_2 Abstract: This paper describes the development of the first test suite for the language direction Portuguese-English. Designed for fine-grained linguistic analysis, the test suite comprises 330 test sentences for 66 linguistic phenomena and 14 linguistic categories. Eight different MT systems were compared using quantitative and qualitative methods via the test suite: DeepL, Google Sheets, Google Translator, Microsoft Translator, Reverso, Systran, Yandex and an internally built NMT system trained over 30 hours on 2,5M sentences. It was found that ambiguity, named entity & terminology and verb valency are the categories where MT systems struggle most. Negation, pronouns, subordination, verb tense/aspect/mood and false friends are the categories where MT systems perform best. ...
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URL: https://underline.io/lecture/48032-49---a-test-suite-for-the-evaluation-of-portuguese-english-machine-translations https://dx.doi.org/10.48448/e5cw-my14
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