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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 Hits 1 – 12 of 12 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 Czech image captioning, machine translation, sentiment analysis and summarization (Neural Monkey models) Libovický, Jindřich; Rosa, Rudolf; Helcl, Jindřich. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2020 BASE Show details 4 On the Language Neutrality of Pre-trained Multilingual Representations ... Libovický, Jindřich; Rosa, Rudolf; Fraser, Alexander. - : arXiv, 2020 BASE Show details 5 How Language-Neutral is Multilingual BERT? ... Libovický, Jindřich; Rosa, Rudolf; Fraser, Alexander. - : arXiv, 2019 Abstract: Multilingual BERT (mBERT) provides sentence representations for 104 languages, which are useful for many multi-lingual tasks. Previous work probed the cross-linguality of mBERT using zero-shot transfer learning on morphological and syntactic tasks. We instead focus on the semantic properties of mBERT. We show that mBERT representations can be split into a language-specific component and a language-neutral component, and that the language-neutral component is sufficiently general in terms of modeling semantics to allow high-accuracy word-alignment and sentence retrieval but is not yet good enough for the more difficult task of MT quality estimation. Our work presents interesting challenges which must be solved to build better language-neutral representations, particularly for tasks requiring linguistic transfer of semantics. ... : 6 pages, 3 figures ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1911.03310 https://dx.doi.org/10.48550/arxiv.1911.03310 BASE Hide details 6 Czech image captioning, machine translation, and sentiment analysis (Neural Monkey models) Libovický, Jindřich; Rosa, Rudolf; Helcl, Jindřich. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018 BASE Show details 7 Machine Translation Evaluation beyond the Sentence Level Libovický, Jindřich; Brovelli (Meyer), Thomas; Cartoni, Bruno. - : European Association for Machine Translation, 2018 BASE Show details 8 Khresmoi Summary Translation Test Data 2.0 Dušek, Ondřej; Hajič, Jan; Hlaváčová, Jaroslava. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 BASE Show details 9 Khresmoi Query Translation Test Data 2.0 Pecina, Pavel; Dušek, Ondřej; Hajič, Jan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 BASE Show details 10 Neural Monkey: An Open-source Tool for Sequence Learning Helcl Jindřich; Libovický Jindřich In: Prague Bulletin of Mathematical Linguistics , Vol 107, Iss 1, Pp 5-17 (2017) (2017) BASE Show details 11 CzEng 1.6: Enlarged Czech-English Parallel Corpus with Processing Tools Dockered Bojar, Ondřej [Verfasser]; Dušek, Ondřej [Verfasser]; Kocmi, Tom [Verfasser]. - Aachen : Universitätsbibliothek der RWTH Aachen, 2016 DNB Subject Category Language Show details 12 KER - Keyword Extractor Libovický, Jindřich. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2016 BASE Show details

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