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1
Pushing the right buttons: adversarial evaluation of quality estimation
In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022)
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2
THEaiTRobot 1.0
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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3
To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation ...
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4
SumeCzech
Straka, Milan; Mediankin, Nikita; Kocmi, Tom. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2020
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5
LanideNN: Multilingual Language Identification on Character Window ...
Kocmi, Tom; Bojar, Ondřej. - : arXiv, 2017
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6
An Exploration of Word Embedding Initialization in Deep-Learning Tasks ...
Kocmi, Tom; Bojar, Ondřej. - : arXiv, 2017
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7
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
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8
Extended Morphosyntactic Testset for Word2Vec
Kocmi, Tom; Bojar, Ondřej. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2016
Abstract: We have created test set for syntactic questions presented in the paper [1] which is more general than Mikolov's [2]. Since we were interested in morphosyntactic relations, we extended only the questions of the syntactic type with exception of nationality adjectives which is already covered completely in Mikolov's test set. We constructed the pairs more or less manually, taking inspiration in the Czech side of the CzEng corpus [3], where explicit morphological annotation allows to identify various pairs of Czech words (different grades of adjectives, words and their negations, etc.). The word-aligned English words often shared the same properties. Another sources of pairs were acquired from various webpages usually written for learners of English. For example for verb tense, we relied on a freely available list of English verbs and their morphological variations. We have included 100-1000 different pairs for each question set. The questions were constructed from the pairs similarly as by Mikolov: generating all possible pairs of pairs. This leads to millions of questions, so we randomly selected 1000 instances per question set, to keep the test set in the same order of magnitude. Additionally, we decided to extend set of questions on opposites to cover not only opposites of adjectives but also of nouns and verbs.
Keyword: English language; syntactic questions
URL: http://hdl.handle.net/11234/1-1730
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