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Source language difficulties in learner translation: Evidence from an error-annotated corpus
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An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers ...
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An exploratory analysis of multilingual word-level quality estimation with cross-lingual transformers
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A sequence labelling approach for automatic analysis of ello: tagging pronouns, antecedents, and connective phrases
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TransQuest at WMT2020: Sentence-Level direct assessment
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In: 1049 ; 1055 (2020)
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TransQuest: Translation quality estimation with cross-lingual transformers
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In: 5070 ; 5081 (2020)
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Abstract:
© 2020 The Authors. Published by International Committee on Computational Linguistics. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://aclanthology.org/2020.coling-main.445/ ; Recent years have seen big advances in the field of sentence-level quality estimation (QE), largely as a result of using neural-based architectures. However, the majority of these methods work only on the language pair they are trained on and need retraining for new language pairs. This process can prove difficult from a technical point of view and is usually computationally expensive. In this paper we propose a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. Our evaluation shows that the proposed methods achieve state-of-the-art results outperforming current open-source quality estimation frameworks when trained on datasets from WMT. In addition, the framework proves very useful in transfer learning settings, especially when dealing with low-resourced languages, allowing us to obtain very competitive results. ; Published version
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Keyword:
cs.AI; cs.CL; cs.LG
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URL: http://hdl.handle.net/2436/624693 https://doi.org/10.18653/v1/2020.coling-main.445
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Intelligent translation memory matching and retrieval with sentence encoders
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In: 175 ; 184 (2020)
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Contributions to the Computational Treatment of Non-literal Language
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What matters more: the size of the corpora or their quality? The case of automatic translation of multiword expressions using comparable corpora.
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RGCL at SemEval-2020 task 6: Neural approaches to definition extraction
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In: 717 ; 723 (2020)
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Automated text simplification as a preprocessing step for machine translation into an under-resourced language
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In: Štajner, Sanja orcid:0000-0002-7780-7035 and Popović, Maja orcid:0000-0001-8234-8745 (2019) Automated text simplification as a preprocessing step for machine translation into an under-resourced language. In: Recent Advances in Natural Language Processing (RANLP 2019), 2-4 Sept 2019, Varna, Bulgaria. (2019)
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Are ambiguous conjunctions problematic for machine translation?
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In: Popović, Maja orcid:0000-0001-8234-8745 and Castilho, Sheila orcid:0000-0002-8416-6555 (2019) Are ambiguous conjunctions problematic for machine translation? In: Recent Advances in Natural Language Processing (RANLP 2019), 2 - 4 Sept 2019, Varna, Bulgaria. (2019)
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Natural Language Generation
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In: Handbook of Computational Linguistics (2nd edition) ; https://hal.archives-ouvertes.fr/hal-02079245 ; Mitkov, Ruslan. Handbook of Computational Linguistics (2nd edition), In press (2019)
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Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions ...
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Summary Refinement through Denoising
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In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (2019)
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Large-Scale Hierarchical Alignment for Data-driven Text Rewriting
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In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (2019)
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Do Online Resources Give Satisfactory Answers to Questions about Meaning and Phraseology?
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RGCL at IDAT: deep learning models for irony detection in Arabic language
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In: 2517 ; 416 ; 425 (2019)
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Bridging the gap: attending to discontinuity in identification of multiword expressions
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