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20172022
most citedTo Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation

82 citations · 143 across the 7 of their papers we have counts for

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12 papers · 1 filter

cs.CL20223 cited

The Reality of Multi-Lingual Machine Translation

Tom Kocmi, Dominik Macháček, Ondřej Bojar

Our book "The Reality of Multi-Lingual Machine Translation" discusses the benefits and perils of using more than two languages in machine translation systems. While focused on the…

cs.CL202182 cited

To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation

Tom Kocmi, Christian Federmann, Roman Grundkiewicz +3

Automatic metrics are commonly used as the exclusive tool for declaring the superiority of one machine translation system's quality over another. The community choice of automatic…

cs.CL20211 cited

On User Interfaces for Large-Scale Document-Level Human Evaluation of Machine Translation Outputs

Roman Grundkiewicz, Marcin Junczys-Dowmunt, Christian Federmann +1

Recent studies emphasize the need of document context in human evaluation of machine translations, but little research has been done on the impact of user interfaces on annotator p…

cs.CL2021

THEaiTRE 1.0: Interactive generation of theatre play scripts

Rudolf Rosa, Tomáš Musil, Ondřej Dušek +13

We present the first version of a system for interactive generation of theatre play scripts. The system is based on a vanilla GPT-2 model with several adjustments, targeting specif…

cs.CL2020

CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20

Ivana Kvapilíková, Tom Kocmi, Ondřej Bojar

This paper presents a description of CUNI systems submitted to the WMT20 task on unsupervised and very low-resource supervised machine translation between German and Upper Sorbian.…

cs.CL2020

Gender Coreference and Bias Evaluation at WMT 2020

Tom Kocmi, Tomasz Limisiewicz, Gabriel Stanovsky

Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as…