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Team ÚFAL at CMCL 2022 Shared Task: Figuring out the correct recipe for predicting Eye-Tracking features using Pretrained Language Models ...
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Pushing the right buttons: adversarial evaluation of quality estimation
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In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022)
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THEaiTRobot 1.0
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Rosa, Rudolf; Dušek, Ondřej; Kocmi, Tom. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2021. : The Švanda Theatre in Smíchov, 2021. : The Academy of Performing Arts in Prague, Theatre Faculty (DAMU), 2021
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Ptakopět data: the dataset for experiments on outbound translation
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Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining ...
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Abstract:
Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages. We propose a novel unsupervised method to derive multilingual sentence embeddings relying only on monolingual data. We first produce a synthetic parallel corpus using unsupervised machine translation, and use it to fine-tune a pretrained cross-lingual masked language model (XLM) to derive the multilingual sentence representations. The quality of the representations is evaluated on two parallel corpus mining tasks with improvements of up to 22 F1 points over vanilla XLM. In addition, we observe that a single synthetic bilingual corpus is able to improve results for other language pairs. ... : ACL SRW 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2105.10419 https://arxiv.org/abs/2105.10419
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CUNI systems for WMT21: Multilingual Low-Resource Translation for Indo-European Languages Shared Task ...
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Lost in Interpreting: Speech Translation from Source or Interpreter? ...
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Sequence Length is a Domain: Length-based Overfitting in Transformer Models ...
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Neural Machine Translation Quality and Post-Editing Performance ...
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End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages ...
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