CoChat
Sign Up
Cited by 539Natural Language Processing TechniquesTopic ModelingSpeech Recognition and Synthesis

Findings of the 2016 Conference on Machine Translation

Open access
Bojar, Ondřej, Chatterjee, Rajen, Federmann, Christian, Graham, Yvette, Haddow, Barry, Huck, Matthias, Jimeno Yepes, Antonio, Koehn, Philipp +13 more·2016·Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers
DOI 10.18653/v1/w16-2301
Open this paper in CoChat — free account

Abstract

Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, Marcos Zampieri. Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers. 2016.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18653/v1/w16-2301

Cites

122 of 122 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • LLMs are particularly good at translating some low-resource language texts to English, such as in Romanian-English translation of WMT'16, where zero-shot or few-shot LLMs can perform better than SOTA fine-tuned model.

    “LLMs are particularly good at translating some low-resource language texts to English texts, such as in the Romanian-English translation of WMT'16 [13], zeroshot or few-shot LLMs can perform better than SOTA fine-tuned model [25].”

    From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [13]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 20

  1. Allauzen, A., Aufrant, L., Burlot, F., Lacroix, O., Knyazeva, E., Lavergne, T., Wisniewski, G., and Yvon, F. (2016). LIMSI@WMT16: Ma- chine Translation of News. In Proceedings of the First Conference on Machine Transla- tion, Berlin, Germany. Association for Compu- tational Linguistics.
  2. Bektas ¸, E., Yilmaz, E., Mermer, C., and Dur- gar El-Kahlout, . (2016). T ÜBTAK SMT Sys- tem Submission for WMT2016. In Proceed- ings of the First Conference on Machine Trans- lation, Berlin, Germany. Association for Com- putational Linguistics.
  3. Bic ¸ici, E. and Way, A. (2015). Referential trans- lation machines for predicting semantic similar- ity. Language Resources and Evaluation, pages 1-27.
  4. Bojar, O., Chatterjee, R., Federmann, C., Had- dow, B., Huck, M., Hokamp, C., Koehn, P., Logacheva, V., Monz, C., Negri, M., Post, M., Scarton, C., Specia, L., and Turchi, M. (2015). Findings of the 2015 workshop on statistical machine translation. In Proceedings of the Tenth Workshop on Statistical Machine Transla- tion, pages 1-46, Lisbon, Portugal. Association for Computational Linguistics.
  5. Bojar, O., Ercegovčević, M., Popel, M., and Zaidan, O. (2011). A grain of salt for the wmt manual evaluation. In Proceedings of the Sixth Workshop on Statistical Machine Translation, pages 1-11, Edinburgh, Scotland. Association for Computational Linguistics.
  6. Callison-Burch, C., Koehn, P., Monz, C., and Zaidan, O. (2011). Findings of the 2011 work- shop on statistical machine translation. In Pro- ceedings of the Sixth Workshop on Statistical Machine Translation, pages 22-64, Edinburgh, Scotland. Association for Computational Lin- guistics.
  7. Chung, J., Cho, K., and Bengio, Y. (2016). NYU- MILA Neural Machine Translation Systems for WMT16. In Proceedings of the First Confer- ence on Machine Translation, Berlin, Germany. Association for Computational Linguistics.
  8. Junczys-Dowmunt, M. and Grundkiewicz, R. (2016). Log-linear Combinations of Mono- lingual and Bilingual Neural Machine Trans- lation Models for Automatic Post-Editing. In Proceedings of the First Conference on Ma- chine Translation. Association for Computa- tional Linguistics.
  9. Kim, H. and Lee, J.-H. (2016). Recurrent Neu- ral Network based Translation Quality Estima- tion. In Proceedings of the First Conference on Machine Translation, Berlin, Germany. Associ- ation for Computational Linguistics.
  10. Liang, P., Taskar, B., and Klein, D. (2006). Align- ment by agreement. In Proceedings of the main conference on Human Language Technol- ogy Conference of the North American Chapter of the Association of Computational Linguis- tics, pages 104-111. Association for Computa- tional Linguistics.
  11. Libovický, J., Helcl, J., Tlustý, M., Bojar, O., and Pecina, P. (2016). CUNI at Post-editing and Multimodal Translation Tasks. In Proceedings of the First Conference on Machine Translation. Association for Computational Linguistics.
  12. Logacheva, V., , and Specia, L. (2015). Phrase- level Quality Estimation for Machine Transla- tion. In Proceedings of the 12th International Workshop on Spoken Language Translation, Da Nang, Vietnam.
  13. Logacheva, V., Blain, F., and Specia, L. (2016a). USFD Phrase-level Quality Estimation Sys- tems. In Proceedings of the First Conference on Machine Translation, Berlin, Germany. As- sociation for Computational Linguistics.
  14. Och, F. J. and Ney, H. (2003). A Systematic Com- parison of Various Statistical Alignment Mod- els. Comput. Linguist., 29(1):19-51.
  15. Rosa, R., Sudarikov, R., Novák, M., Popel, M., and Bojar, O. (2016). Dictionary-based Do- main Adaptation of MT Systems without Re- training. In Proceedings of the First Conference on Machine Translation, Berlin, Germany. As- sociation for Computational Linguistics.
  16. Sagemo, O. and Stymne, S. (2016). The UU Sub- mission to the Machine Translation Quality Es- timation Task. In Proceedings of the First Con- ference on Machine Translation, Berlin, Ger- many. Association for Computational Linguis- tics.
  17. Sennrich, R., Haddow, B., and Birch, A. (2015). Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909. · arXiv
  18. Sennrich, R., Haddow, B., and Birch, A. (2016). Edinburgh Neural Machine Translation Sys- tems for WMT 16. In Proceedings of the First Conference on Machine Translation, Berlin, Germany. Association for Computational Lin- guistics.
  19. Sudarikov, R., Popel, M., Bojar, O., Burchardt, A., and Klejch, O. (2016). Using MT-ComparEval. In Translation Evaluation: From Fragmented Tools and Data Sets to an Integrated Ecosystem, pages 76-82.
  20. Williams, P., Sennrich, R., Nadejde, M., Huck, M., Haddow, B., and Bojar, O. (2016). Ed- inburgh's Statistical Machine Translation Sys- tems for WMT16. In Proceedings of the First Conference on Machine Translation, Berlin, Germany. Association for Computational Lin- guistics.

Cited by

Sorted by citations

Related

Authors
Bojar, O., Chatterjee, R., Federmann, C., Graham, Y., Haddow, B., Huck, M., Jimeno Yepes, A., Koehn, P., Logacheva, V., Monz, C., Negri, M., Neveol, A., Neves, M., Popel, M., Post, M., Rubino, R., Scarton, C., Specia, L., Turchi, M., Verspoor, K., & Zampieri, M.
Affiliations
  • Ludwig-Maximilians-Universität München (DE)
  • Microsoft (United States) (US)
  • Dublin City University (IE)
  • University of Edinburgh (GB)
  • University of Sheffield (GB)
  • University of Amsterdam (NL)
  • Fondazione Bruno Kessler (IT)
  • Hasso Plattner Institute (DE)
  • Charles University (CZ)
  • Johns Hopkins University (US)
  • The University of Melbourne (AU)
ORCID
Venue
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers
Publisher
Association for Computational Linguistics
Published
2016
Pages
131-198
DOI
10.18653/v1/w16-2301
OpenAlex
W2512924740
Cited by
539
References
122
Citations by year
2026: 7 · 2025: 12 · 2024: 24 · 2023: 57 · 2022: 54 · 2021: 81 · 2020: 62 · 2019: 64 · 2018: 89 · 2017: 75 · 2016: 13
FWCI
43.29
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference on Machine Translation (WMT) (publishedVersion, cc-by)
  • RWTH Publications (RWTH Aachen) (publishedVersion, other-oa)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
  • UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
  • Institutional Research Information System (Università degli Studi di Trento) (submittedVersion, cc-by-nc-sa)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders
  • European Commission (H2020)
  • Horizon 2020 Framework Programme
  • Air Force Research Laboratory
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords
machine translation, shared task, translation quality assessment, low-resource languages, multilingual translation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review