Name
Abstract | ||
|---|---|---|
Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk rather than a word as the basic translation unit, local (intra-chunk) and global (inter-chunk) word orders and dependencies can be easily modeled. The chunk structure, despite its importance, has not been considered in the decoders used for neural machine translation (NMT). In this paper, we propose chunk-based decoders for NMT, each of which consists of a chunk-level decoder and a word-level decoder. The chunk-level decoder models global dependencies while the word-level decoder decides the local word order in a chunk. To output a target sentence, the chunk-level decoder generates a chunk representation containing global information, which the word-level decoder then uses as a basis to predict the words inside the chunk. Experimental results show that our proposed decoders can significantly improve translation performance in a WAT '16 English-to-Japanese translation task. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/P17-1174 | PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1 |
Field | DocType | Volume |
Computer science,Machine translation,Soft-decision decoder,Natural language processing,Artificial intelligence | Conference | P17-1 |
Citations | PageRank | References |
5 | 0.48 | 20 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shonosuke Ishiwatari | 1 | 6 | 2.87 |
| JingTao Yao | 2 | 1217 | 83.16 |
| Shujie Liu | 3 | 338 | 37.84 |
| Mu Li | 4 | 958 | 66.10 |
| Ming Zhou | 5 | 4262 | 251.74 |
| Naoki Yoshinaga | 6 | 8 | 3.95 |
| Masaru Kitsuregawa | 7 | 3188 | 831.46 |
| Weijia Jia | 8 | 2656 | 221.35 |