Overview of the 5th Workshop on Asian Translation

By Toshiaki Nakazawa, Shohei Higashiyama, Chenchen Ding, Hideya Mino, Isao Goto, Hideto Kazawa, Yusuke Oda, Graham Neubig, Sadao Kurohashi. 2019

46 citations recorded by the source corpus.

This paper presents the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) including Ja↔En, Ja↔Zh scientific paper translation subtasks, Ja↔En, Ja↔Ko, Ja↔En patent translation subtasks, Hi↔En, My↔En, Km↔En, Ta↔En mixed domain subtasks and Ru↔Ja news commentary translation task. For the WAT2019, 25 teams participated in the shared tasks. We also received 10 research paper submissions out of which 61 were accepted. About 400 translation results were submitted to the automatic evaluation server, and selected submis- sions were manually evaluated.

Subfields: Artificial Intelligence, Language and Linguistics, Evaluation & Benchmarks, Machine Translation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Language and Linguistics

In Evaluation & Benchmarks

In Machine Translation

Citations in the corpus

Facebook AI’s WAT19 Myanmar-English Translation Task Submission (2019, methodology): “…(En → My) BLEU Adequacy FBAI 39.3 3.9 FBAI 36.8 - Team A 31.3 2.4 Team B 30.8 2.7 Team C 30.8 - Team D 28.2 - Team F 25.9 - Team G 22.5 - Team H 20.9 1.1 Team I 19.9 - the four teams that scored the best according to BLEU, and they performed a JPO adequacy human evaluation (Nakazawa et al., 2018).”
A Survey of Multilingual Neural Machine Translation (2019, background): “Recently, shared tasks with a focus on multilingual translation have been conducted at IWSLT (Cettolo et al., 2017), WAT (Nakazawa et al., 2018) and WMT (Bojar et al., 2018); so common benchmarks are available.”
Findings of the Third Workshop on Neural Generation and Translation (2019, methodology): “The efficiency shared task for WNGT (inspired by the “small NMT” task at the Workshop on Asian Translation (Nakazawa et al., 2017)) was focused on creating systems for NMT that are not only accurate, but also efficient.”