Name
Abstract | ||
|---|---|---|
This paper describes the system of our team (NHK) for the WAT 2021 Japanese <-> English restricted machine translation task. In this task, the aim is to improve quality while maintaining consistent terminology for scientific paper translation. This task has a unique feature, where some words in a target sentence are given in addition to a source sentence. In this paper, we use a lexically-constrained neural machine translation (NMT), which concatenates the source sentence and constrained words with a special token to input them into the encoder of NMT. The key to the successful lexically-constrained NMT is the way to extract constraints from a target sentence of training data. We propose two extraction methods: proper-noun constraint and mistranslated-word constraint. These two methods consider the importance of words and fallibility of NMT, respectively. The evaluation results demonstrate the effectiveness of our lexical-constraint method. |
Year | Venue | DocType |
|---|---|---|
2021 | WAT 2021: THE 8TH WORKSHOP ON ASIAN TRANSLATION | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hideya Mino | 1 | 1 | 2.06 |
| Kazutaka Kinugawa | 2 | 0 | 0.34 |
| Hitoshi Ito | 3 | 0 | 1.69 |
| Isao Goto | 4 | 0 | 0.34 |
| Ichiro Yamada | 5 | 87 | 17.38 |
| Takenobu Tokunaga | 6 | 0 | 0.34 |