no code implementations • IWSLT 2016 • M. Amin Farajian, Rajen Chatterjee, Costanza Conforti, Shahab Jalalvand, Vevake Balaraman, Mattia A. Di Gangi, Duygu Ataman, Marco Turchi, Matteo Negri, Marcello Federico
They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words.
no code implementations • AMTA 2016 • Rajen Chatterjee, Mihael Arcan, Matteo Negri, Marco Turchi
In recent years, several end-to-end online translation systems have been proposed to successfully incorporate human post-editing feedback in the translation workflow.
no code implementations • WMT (EMNLP) 2021 • Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ondřej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-Jussa, Cristina España-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin, Marcos Zampieri
This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021. In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories.
no code implementations • WMT (EMNLP) 2020 • Rajen Chatterjee, Markus Freitag, Matteo Negri, Marco Turchi
Due to i) the different source/domain of data compared to the past (Wikipedia vs Information Technology), ii) the different quality of the initial translations to be corrected and iii) the introduction of a new language pair (English-Chinese), this year’s results are not directly comparable with last year’s round.
no code implementations • 28 Jan 2025 • Zilu Tang, Rajen Chatterjee, Sarthak Garg
Specifically, we introduce a data creation framework to generate hallucination focused preference datasets.
no code implementations • WS 2019 • Xiangkai Zeng, Sarthak Garg, Rajen Chatterjee, Udhyakumar Nallasamy, Matthias Paulik
Finally, we propose a neural extension for an AL sampling method used in the context of phrase-based MT - Round Trip Translation Likelihood (RTTL).
no code implementations • 18 Oct 2019 • Rajen Chatterjee
Automatic Post-Editing (APE) aims to correct systematic errors in a machine translated text.
no code implementations • WS 2019 • Rajen Chatterjee, Christian Federmann, Matteo Negri, Marco Turchi
Seven teams participated in the English-German task, with a total of 18 submitted runs.
no code implementations • WS 2018 • Rajen Chatterjee, Matteo Negri, Raphael Rubino, Marco Turchi
In the former subtask, characterized by original translations of lower quality, top results achieved impressive improvements, up to -6. 24 TER and +9. 53 BLEU points over the baseline {``}\textit{do-nothing}{''} system.
no code implementations • EMNLP 2018 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
no code implementations • NAACL 2018 • Judith Gaspers, Penny Karanasou, Rajen Chatterjee
The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests.
no code implementations • LREC 2018 • Matteo Negri, Marco Turchi, Rajen Chatterjee, Nicola Bertoldi
eSCAPE consists of millions of entries in which the MT element of the training triplets has been obtained by translating the source side of publicly-available parallel corpora, and using the target side as an artificial human post-edit.
no code implementations • WS 2017 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Shu-Jian Huang, Matthias Huck, Philipp Koehn, Qun Liu, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Raphael Rubino, Lucia Specia, Marco Turchi
no code implementations • EACL 2017 • Rajen Chatterjee, Gebremedhen Gebremelak, Matteo Negri, Marco Turchi
Automatic post-editing (APE) for machine translation (MT) aims to fix recurrent errors made by the MT decoder by learning from correction examples.
no code implementations • WS 2016 • Ond{\v{r}}ej Bojar, Christian Buck, Rajen Chatterjee, Christian Federmann, Liane Guillou, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Pavel Pecina, Martin Popel, Philipp Koehn, Christof Monz, Matteo Negri, Matt Post, Lucia Specia, Karin Verspoor, J{\"o}rg Tiedemann, Marco Turchi
no code implementations • WS 2016 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, Marcos Zampieri
no code implementations • WS 2015 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Barry Haddow, Matthias Huck, Chris Hokamp, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Carolina Scarton, Lucia Specia, Marco Turchi
no code implementations • LREC 2014 • Anoop Kunchukuttan, Abhijit Mishra, Rajen Chatterjee, Ritesh Shah, Pushpak Bhattacharyya
We present a compendium of 110 Statistical Machine Translation systems built from parallel corpora of 11 Indian languages belonging to both Indo-Aryan and Dravidian families.