no code implementations • loresmt (AACL) 2020 • Atul Kr. Ojha, Valentin Malykh, Alina Karakanta, Chao-Hong Liu
This paper presents the findings of the LoResMT 2020 Shared Task on zero-shot translation for low resource languages.
no code implementations • 6 Nov 2023 • Longyue Wang, Zhaopeng Tu, Yan Gu, Siyou Liu, Dian Yu, Qingsong Ma, Chenyang Lyu, Liting Zhou, Chao-Hong Liu, Yufeng Ma, WeiYu Chen, Yvette Graham, Bonnie Webber, Philipp Koehn, Andy Way, Yulin Yuan, Shuming Shi
To foster progress in this domain, we hold a new shared task at WMT 2023, the first edition of the Discourse-Level Literary Translation.
no code implementations • MTSummit 2021 • Atul Kr. Ojha, Chao-Hong Liu, Katharina Kann, John Ortega, Sheetal Shatam, Theodorus Fransen
Maximum system performance was computed using BLEU and follow as 36. 0 for English--Irish, 34. 6 for Irish--English, 24. 2 for English--Marathi, and 31. 3 for Marathi--English.
no code implementations • LREC 2020 • Alberto Poncelas, Wichaya Pidchamook, Chao-Hong Liu, James Hadley, Andy Way
Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality.
no code implementations • PACLIC 2018 • Atul Kr. Ojha, Koel Dutta Chowdhury, Chao-Hong Liu, Karan Saxena
This paper presents the system description of Machine Translation (MT) system(s) for Indic Languages Multilingual Task for the 2018 edition of the WAT Shared Task.
no code implementations • WS 2018 • Catarina Cruz Silva, Chao-Hong Liu, Alberto Poncelas, Andy Way
Data selection is a process used in selecting a subset of parallel data for the training of machine translation (MT) systems, so that 1) resources for training might be reduced, 2) trained models could perform better than those trained with the whole corpus, and/or 3) trained models are more tailored to specific domains.
no code implementations • 5 Jun 2018 • Chao-Hong Liu, Declan Groves, Akira Hayakawa, Alberto Poncelas, Qun Liu
Understanding and being able to react to customer feedback is the most fundamental task in providing good customer service.
no code implementations • LREC 2018 • Siyou Liu, Long-Yue Wang, Chao-Hong Liu
The approach we used in this paper also shows a good example on how to boost performance of MT systems for low-resource language pairs.
no code implementations • IJCNLP 2017 • Chao-Hong Liu, Yasufumi Moriya, Alberto Poncelas, Declan Groves
This document introduces the IJCNLP 2017 Shared Task on Customer Feedback Analysis.
no code implementations • WS 2017 • Carla Parra Escart{\'\i}n, Wessel Reijers, Teresa Lynn, Joss Moorkens, Andy Way, Chao-Hong Liu
Shared tasks are increasingly common in our field, and new challenges are suggested at almost every conference and workshop.