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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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ESIC 1.0 -- Europarl Simultaneous Interpreting Corpus
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Abstract:
ESIC (Europarl Simultaneous Interpreting Corpus) is a corpus of 370 speeches (10 hours) in English, with manual transcripts, transcribed simultaneous interpreting into Czech and German, and parallel translations. The corpus contains source English videos and audios. The interpreters' voices are not published within the corpus, but there is a tool that downloads them from the web of European Parliament, where they are publicly avaiable. The transcripts are equipped with metadata (disfluencies, mixing voices and languages, read or spontaneous speech, etc.), punctuated, and with word-level timestamps. The speeches in the corpus come from the European Parliament plenary sessions, from the period 2008-11. Most of the speakers are MEP, both native and non-native speakers of English. The corpus contains metadata about the speakers (name, surname, id, fraction) and about the speech (date, topic, read or spontaneous). The current version of ESIC is v1.0. It has validation and evaluation parts.
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Keyword:
ASR evaluation; automatic machine translation evaluation; Europarl; interpreting; simultaneous interpreting
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URL: http://hdl.handle.net/11234/1-3719
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Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining ...
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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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