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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 Hits 1 – 20 of 33 1 Combining Static and Contextualised Multilingual Embeddings ... Hämmerl, Katharina; Libovický, Jindřich; Fraser, Alexander. - : arXiv, 2022 BASE Show details 2 Do Multilingual Language Models Capture Differing Moral Norms? ... Hämmerl, Katharina; Deiseroth, Björn; Schramowski, Patrick. - : arXiv, 2022 BASE Show details 3 Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies ... Marco, Marion Weller-Di; Huck, Matthias; Fraser, Alexander. - : arXiv, 2022 BASE Show details 4 Pushing the right buttons: adversarial evaluation of quality estimation Specia, Lucia; Fomicheva, Marina; Ranasinghe, Tharindu; Orasan, Constantin; Kanojia, Diptesh; Blain, Frederic In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022) Abstract: © (2021) The Authors. Published by Association for 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/2021.wmt-1.67 ; Current Machine Translation (MT) systems achieve very good results on a growing variety of language pairs and datasets. However, they are known to produce fluent translation outputs that can contain important meaning errors, thus undermining their reliability in practice. Quality Estimation (QE) is the task of automatically assessing the performance of MT systems at test time. Thus, in order to be useful, QE systems should be able to detect such errors. However, this ability is yet to be tested in the current evaluation practices, where QE systems are assessed only in terms of their correlation with human judgements. In this work, we bridge this gap by proposing a general methodology for adversarial testing of QE for MT. First, we show that despite a high correlation with human judgements achieved by the recent SOTA, certain types of meaning errors are still problematic for QE to detect. Second, we show that on average, the ability of a given model to discriminate between meaningpreserving and meaning-altering perturbations is predictive of its overall performance, thus potentially allowing for comparing QE systems without relying on manual quality annotation. Keyword: adversarial evaluation; machine translation; quality estimation URL: http://hdl.handle.net/2436/624376 BASE Hide details 5 Do not neglect related languages: The case of low-resource Occitan cross-lingual word embeddings ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Fraser, Alexander; Hangya, Viktor. - : Underline Science Inc., 2021 BASE Show details 6 Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation ... Chronopoulou, Alexandra; Stojanovski, Dario; Fraser, Alexander. - : arXiv, 2021 BASE Show details 7 Anchor-based Bilingual Word Embeddings for Low-Resource Languages ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Eder, Tobias; Fraser, Alexander. - : Underline Science Inc., 2021 BASE Show details 8 Improving Machine Translation of Rare and Unseen Word Senses ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Dario; Fraser, Alexander. - : Underline Science Inc., 2021 BASE Show details 9 Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation ... NAACL 2021 2021; Chronopoulou, Alexandra; Fraser, Alexander. - : Underline Science Inc., 2021 BASE Show details 10 Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction ... Severini, Silvia; Hangya, Viktor; Fraser, Alexander. - : Universitätsbibliothek der Ludwig-Maximilians-Universität München, 2020 BASE Show details 11 Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction Fraser, Alexander; Severini, Silvia; Schütze, Hinrich. - 2020 BASE Show details 12 Pragmatic information in translation: a corpus-based study of tense and mood in English and German ... Ramm, Anita; Lapshinova-Koltunski, Ekaterina; Fraser, Alexander. - : arXiv, 2020 BASE Show details 13 ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation ... The 28th International Conference on Computational Linguistics 2020; Fraser, Alexander; Krojer, Benno. - : Underline Science Inc., 2020 BASE Show details 14 Anchor-based Bilingual Word Embeddings for Low-Resource Languages ... Eder, Tobias; Hangya, Viktor; Fraser, Alexander. - : arXiv, 2020 BASE Show details 15 On the Language Neutrality of Pre-trained Multilingual Representations ... Libovický, Jindřich; Rosa, Rudolf; Fraser, Alexander. - : arXiv, 2020 BASE Show details 16 How Language-Neutral is Multilingual BERT? ... Libovický, Jindřich; Rosa, Rudolf; Fraser, Alexander. - : arXiv, 2019 BASE Show details 17 Modeling the position and inflection of verbs in English to German machine translation Fraser, Alexander [Akademischer Betreuer]; Ramm, Anita [Verfasser]. - Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2018 DNB Subject Category Language Show details 18 Embedding Learning Through Multilingual Concept Induction Fraser, Alexander; Zhao, Mengjie; Dufter, Philipp. - : Ludwig-Maximilians-Universität München, 2018 BASE Show details 19 Embedding Learning Through Multilingual Concept Induction ... Dufter, Philipp; Zhao, Mengjie; Schmitt, Martin. - : arXiv, 2018 BASE Show details 20 Embedding Learning Through Multilingual Concept Induction ... Dufter, Philipp; Zhao, Mengjie; Schmitt, Martin. - : Universitätsbibliothek der Ludwig-Maximilians-Universität München, 2018 BASE Show details Page: 1 2

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