Overview of the 1st Workshop on Asian Translation

By Toshiaki Nakazawa, Hideya Mino, Isao Goto, Sadao Kurohashi, Eiichiro Sumita. 2014

76 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (2018, methodology): “, 2016) is a de facto standard subword segmentation algorithm applied to many NMT systems and achieving top translation quality in several shared tasks (Denkowski and Neubig, 2017; Nakazawa et al., 2017).”
Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016 (2016, methodology): “In this paper, we describe NMT systems for the Japanese-English scientific paper translation task of the Workshop on Asian Translation (WAT) 2016 (Nakazawa et al., 2016a).”
Tree-to-Sequence Attentional Neural Machine Translation (2016, methodology): “Comparison with the SMT models PB, HPB and T2S are the baseline SMT systems in WAT’15: a phrase-based model, a hierarchical phrase-based model, and a tree-to-string model, respectively (Nakazawa et al., 2015). The best model in WAT’15 is Neubig et al. (2015)’s treeto-string SMT model enhanced with r”
A Survey of Domain Adaptation for Neural Machine Translation (2018, methodology): “Sajjad et al. (2017) further compare different methods for training a multi-domain system.”
Neural Machine Translation with Source-Side Latent Graph Parsing (2017, result): “Table 5 shows our results on the test data, and the previous best results summarized in Nakazawa et al. (2016a) and the WAT website5 are also shown.”
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.”
Overview of the 4th Workshop on Asian Translation (2017, unknown): “Following the success of the previous workshops WAT2014-WAT2017 (Nakazawa et al., 2014; Nakazawa et al., 2015; Nakazawa et al., 2016; Nakazawa et al., 2017), WAT2018 brings together machine translation researchers and users to try, evaluate, share and discuss brand-new ideas of machine translation.”
Generalizing and Hybridizing Count-based and Neural Language Models (2016, background): “(2010),3 and the first 100k sentences in the English side of the ASPEC corpus (Nakazawa et al., 2015)4 (details in Tab.”
Similar Southeast Asian Languages: Corpus-Based Case Study on Thai-Laotian and Malay-Indonesian (2016, background): “6 and 7 are based on the patent data from WAT2015’s Japanese-Korean task (Nakazawa et al., 2015), shown here for comparison.”
Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation (2016, unknown): “Table 4 shows the results of two methods submitted for the shared task at WAT 2016 (Nakazawa et al., 2016a).”
ASPEC: Asian Scientific Paper Excerpt Corpus (2016, unknown): “The first workshop was held in 2014 (Nakazawa et al., 2014) where the ASPEC was centered as the official dataset for the scientific paper translation subtasks.”
EDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints (2020, background): “, 2014), and EnglishJapanese (En-Ja) from WAT2017 Small-NMT Task (Nakazawa et al., 2017).”
Overview of the 5th Workshop on Asian Translation (2019, methodology): “Following the success of the previous workshops WAT2014-WAT2017 (Nakazawa et al., 2014; Nakazawa et al., 2015; Nakazawa et al., 2016; Nakazawa et al., 2017), WAT2018 brings together machine translation researchers and users to try, evaluate, share and discuss brand-new ideas of machine translation.”