Author of the publication

Proceedings of the LREC 2016 Workshop Translation Evaluation: From Fragmented Tools and Data Sets to an Integrated Ecosystem

, , , , , , , , , , , , , , and (Eds.) Portorož, Slovenia, (May 2016)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

 

Other publications of authors with the same name

Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation., and . W-NUT@EMNLP, page 328-336. Association for Computational Linguistics, (2019)Multimodal Machine Translation through Visuals and Speech., , , , , , and . CoRR, (2019)Multimodal machine translation through visuals and speech., , , , , , and . Mach. Transl., 34 (2): 97-147 (2020)Exploiting Objective Annotations for Minimising Translation Post-editing Effort.. EAMT, European Association for Machine Translation, (2011)Estimating the Sentence-Level Quality of Machine Translation Systems., , , , and . EAMT, European Association for Machine Translation, (2009)Cross-lingual Sentence Compression for Subtitles., , and . EAMT, page 103-110. European Association for Machine Translation, (2012)Learning an Expert from Human Annotations in Statistical Machine Translation: the Case of Out-of-Vocabulary Words., , , and . EAMT, European Association for Machine Translation, (2010)Findings of the WMT 2021 shared task on quality estimation, , , , , , and . (2021)We report the results of the WMT 2021 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels. This edition focused on two main novel additions: (i) prediction for unseen languages, i.e. zero-shot settings, and (ii) prediction of sentences with catastrophic errors. In addition, new data was released for a number of languages, especially post-edited data. Participating teams from 19 institutionssubmitted altogether 1263 systems to different task variants and language pairs..Imperial College London Submission to VATEX Video Captioning Task., , , , and . CoRR, (2019)WMDO: Fluency-based Word Mover's Distance for Machine Translation Evaluation., , and . WMT (2), page 494-500. Association for Computational Linguistics, (2019)