Presentation information

General Session

General Session » J-9 Natural language processing, information retrieval

[1E5-GS-9] Natural language processing, information retrieval: Estimate and analysis

Tue. Jun 9, 2020 5:20 PM - 6:40 PM Room E (jsai2020online-5)


5:40 PM - 6:00 PM

[1E5-GS-9-02] Analyzing Translation Accuracy of Concatenation-based Multi-source Neural Machine Translation

〇Tomoya Isobe1, Po-Hsuan Hung1, Shohei Iida1, Yizhen Wei1, Takehito Utsuro1, Masaaki Nagata2 (1. University of Tsukuba, 2. NTT)

Keywords:neural machine translation, multi-source, concatenation-based, translation accuracy analysis, three-language parallel corpus

In this paper, we study a concatenation-based multi-source neural machine translation (NMT) model trained with three-language parallel corpus. We show that the concatenation-based multi-source NMT model where a parallel English and Chinese sentences are input to the model as the source sentences improves the BLEU score of the single-source NMT where only English or Chinese source sentence is input to the model. Among major phenomena where the BLEU improves when translating from the source English sentence than from the source Chinese sentence are translation of Katakana loanwords, tense, and particles, etc., while, in the translation of Chinese words when the Chinese and Japanese words share an identical Chinese character, the BLEU improves when translating from the source Chinese sentence than from the source English sentence. We then show that, in the translation by the concatenation-based multi-source NMT model, the BLEU improves the most by correctly incorporating translation of both types of phenomena in a complementary style.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.