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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5...9 Hits 1 – 20 of 162 1 Source language difficulties in learner translation: Evidence from an error-annotated corpus Kunilovskaia, Mariia; Ilyushchenya, Tatyana; Morgoun, Natalia. - : John Benjamins Publishing, 2022 BASE Show details 2 An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers ... Ranasinghe, Tharindu; Orasan, Constantin; Mitkov, Ruslan. - : arXiv, 2021 BASE Show details 3 An exploratory analysis of multilingual word-level quality estimation with cross-lingual transformers Mitkov, Ruslan; Orasan, Constantin; Ranasinghe, Tharindu. - : Association for Computational Linguistics, 2021 BASE Show details 4 A sequence labelling approach for automatic analysis of ello: tagging pronouns, antecedents, and connective phrases Parodi, Giovanni; Evans, Richard; Ha, Le An. - : Springer, 2021 BASE Show details 5 TransQuest at WMT2020: Sentence-Level direct assessment Mitkov, Ruslan; Ranasinghe, Tharindu; Orasan, Constantin In: 1049 ; 1055 (2020) BASE Show details 6 TransQuest: Translation quality estimation with cross-lingual transformers Ranasinghe, Tharindu; Orasan, Constantin; Mitkov, Ruslan In: 5070 ; 5081 (2020) BASE Show details 7 Intelligent translation memory matching and retrieval with sentence encoders Orasan, Constantin; Ranasinghe, Tharindu; Mitkov, Ruslan In: 175 ; 184 (2020) BASE Show details 8 Contributions to the Computational Treatment of Non-literal Language Rohanian, Omid. - : University of Wolverhampton, 2020 BASE Show details 9 What matters more: the size of the corpora or their quality? The case of automatic translation of multiword expressions using comparable corpora. Mitkov, Ruslan; Taslimipoor, Shiva. - : John Benjamins, 2020 BASE Show details 10 Gender variation in Gulf Pidgin Arabic Albaqawi, Najah Salem. - : University of Wolverhampton, 2020 BASE Show details 11 RGCL at SemEval-2020 task 6: Neural approaches to definition extraction Plum, Alistair; Ranasinghe, Tharindu; Orasan, Constantin... In: 717 ; 723 (2020) BASE Show details 12 Automated text simplification as a preprocessing step for machine translation into an under-resourced language Štajner, Sanja; Popović, Maja 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) BASE Show details 13 Are ambiguous conjunctions problematic for machine translation? Popović, Maja; Castilho, Sheila 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) Abstract: The translation of ambiguous words still poses challenges for machine translation. In this work, we carry out a systematic quantitative analysis regarding the ability of different machine translation systems to disambiguate the source language conjunctions “but” and “and”. We evaluate specialised test sets focused on the translation of these two conjunctions. The test sets contain source languages that do not distinguish different variants of the given conjunction, whereas the target languages do. In total, we evaluate the conjunction “but” on 20 translation outputs, and the conjunction “and” on 10. All machine translation systems almost perfectly recognise one variant of the target conjunction, especially for the source conjunction “but”. The other target variant, however, represents a challenge for machine translation systems, with accuracy varying from 50% to 95% for “but” and from 20% to 57% for “and”. The major error for all systems is replacing the correct target variant with the opposite one. Keyword: Machine translating URL: http://doras.dcu.ie/24476/ BASE Hide details 14 Natural Language Generation Zock, Michael; Bateman, J. 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) BASE Show details 15 Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions ... Rohanian, Omid; Taslimipoor, Shiva; Kouchaki, Samaneh. - : arXiv, 2019 BASE Show details 16 Summary Refinement through Denoising Nikolov, Nikola I.; Calmanovici, Alessandro; Hahnloser, Richard H.R. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (2019) BASE Show details 17 Large-Scale Hierarchical Alignment for Data-driven Text Rewriting Nikolov, Nikola I.; Hahnloser, Richard H.R. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (2019) BASE Show details 18 Do Online Resources Give Satisfactory Answers to Questions about Meaning and Phraseology? Hanks, Patrick; Franklin, Emma. - : Springer, 2019 BASE Show details 19 RGCL at IDAT: deep learning models for irony detection in Arabic language Mandhari, Salim; Mohamed, Emad; Saadany, Hadeel... In: 2517 ; 416 ; 425 (2019) BASE Show details 20 Bridging the gap: attending to discontinuity in identification of multiword expressions Mitkov, Ruslan; Kouchaki, Samaneh; Taslimipoor, Shiva. - : Association for Computational Linguistics, 2019 BASE Show details Page: 1 2 3 4 5...9

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