1 code implementation • COLING 2020 • Maja Popovi{\'c}
Also, it is not restricted to MT output, but can be used for other types of generated text.
no code implementations • CONLL 2020 • Maja Popovi{\'c}
This work presents a detailed analysis of translation errors perceived by readers as comprehensibility and/or adequacy issues.
no code implementations • LREC 2020 • Sheila Castilho, Maja Popovi{\'c}, Andy Way
Despite increasing efforts to improve evaluation of machine translation (MT) by going beyond the sentence level to the document level, the definition of what exactly constitutes a {``}document level{''} is still not clear.
no code implementations • RANLP 2019 • Maja Popovi{\'c}, Sheila Castilho
In total, we evaluate the conjunction {``}but{''} on 20 translation outputs, and the conjunction {``}and{''} on 10.
no code implementations • RANLP 2019 • Alberto Poncelas, Maja Popovi{\'c}, Dimitar Shterionov, Gideon Maillette de Buy Wenniger, Andy Way
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training.
no code implementations • RANLP 2019 • Sanja {\v{S}}tajner, Maja Popovi{\'c}
We use the state-of-the-art automatic text simplification (ATS) system for lexically and syntactically simplifying source sentences, which are then translated with two state-of-the-art English-to-Serbian MT systems, the phrase-based MT (PBMT) and the neural MT (NMT).
1 code implementation • WS 2019 • Pintu Lohar, Maja Popovi{\'c}, Andy Way
This paper reports the results of the first experiment dealing with the challenges of building a machine translation system for user-generated content involving a complex South Slavic language.
no code implementations • WS 2019 • Maja Popovi{\'c}
Qualitative manual inspection of translation hypotheses shown that highly ranked systems generally produce translations with high adequacy and fluency, meaning that these systems are not only capable of capturing the right conjunction whereas the rest of the translation hypothesis is poor.
no code implementations • WS 2018 • Maja Popovi{\'c}
The system was ranked in the middle range for all English texts, as third of fourteen submissions for German, and as tenth of seventeen submissions for Spanish.
no code implementations • WS 2016 • Maja Popovi{\'c}, Mihael Ar{\v{c}}an, Filip Klubi{\v{c}}ka
This work explores the obstacles for machine translation systems between closely related South Slavic languages, namely Croatian, Serbian and Slovenian.
no code implementations • WS 2016 • Maja Popovi{\'c}, Kostadin Cholakov, Valia Kordoni, Nikola Ljube{\v{s}}i{\'c}
Massive Open Online Courses have been growing rapidly in size and impact.
no code implementations • LREC 2016 • Nora Aranberri, Eleftherios Avramidis, Aljoscha Burchardt, Ond{\v{r}}ej Klejch, Martin Popel, Maja Popovi{\'c}
This work addresses the need to aid Machine Translation (MT) development cycles with a complete workflow of MT evaluation methods.
no code implementations • LREC 2016 • Maja Popovi{\'c}, Mihael Ar{\v{c}}an
We present a freely available corpus containing source language texts from different domains along with their automatically generated translations into several distinct morphologically rich languages, their post-edited versions, and error annotations of the performed post-edit operations.
no code implementations • LREC 2014 • Eleftherios Avramidis, Aljoscha Burchardt, Sabine Hunsicker, Maja Popovi{\'c}, Cindy Tscherwinka, David Vilar, Hans Uszkoreit
Human translators are the key to evaluating machine translation (MT) quality and also to addressing the so far unanswered question when and how to use MT in professional translation workflows.
no code implementations • LREC 2012 • Jan Berka, Ond{\v{r}}ej Bojar, Mark Fishel, Maja Popovi{\'c}, Daniel Zeman
We present a complex, open source tool for detailed machine translation error analysis providing the user with automatic error detection and classification, several monolingual alignment algorithms as well as with training and test corpus browsing.
no code implementations • LREC 2012 • Eleftherios Avramidis, Aljoscha Burchardt, Christian Federmann, Maja Popovi{\'c}, Cindy Tscherwinka, David Vilar
Significant breakthroughs in machine translation only seem possible if human translators are taken into the loop.
no code implementations • LREC 2012 • Mark Fishel, Ond{\v{r}}ej Bojar, Maja Popovi{\'c}
Recently the first methods of automatic diagnostics of machine translation have emerged; since this area of research is relatively young, the efforts are not coordinated.