Name
Abstract | ||
|---|---|---|
Existing lexically constrained machine translation employs data augmentation and incorporates lexical constraints during decoding period, which requires a bilingual dictionary or costs much decoding time. In this paper, we propose a simple but effective method to leverage lexical constraints. We use separate encoders to encode source sentence and lexical constraints, with self-attention layer mask to disentangle the two encoding sub-tasks. Our method does not require bilingual dictionaries or modify decoding process. Experiments on WMT 2016 English-German (En-De) and IWSLT 2017 English-Chinese (En-Zh) datasets show that our method gains improvement compared to baseline models. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9533673 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
DocType | ISSN | Citations |
Conference | 2161-4393 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiyuan Zeng | 1 | 0 | 0.34 |
| Cong Liu | 2 | 586 | 30.47 |