Abstract
We present a document-level neural machine translation model which takes both
source and target document context into account using memory networks. We
model the problem as a structured prediction problem with interdependencies
among the observed and hidden variables, i.e., the source sentences and their unobserved target translations in the document. The resulting structured prediction problem is tackled with a neural translation model equipped with two memory components, one each for the source and target side, to capture the documental interdependencies. We train the model end-to-end, and propose an iterative decoding algorithm based on block coordinate descent. Experimental results of English translations from French, German, and Estonian documents show that our model is effective in exploiting both source and target document context, and statistically significantly outperforms the previous work in terms of BLEU and METEOR.
source and target document context into account using memory networks. We
model the problem as a structured prediction problem with interdependencies
among the observed and hidden variables, i.e., the source sentences and their unobserved target translations in the document. The resulting structured prediction problem is tackled with a neural translation model equipped with two memory components, one each for the source and target side, to capture the documental interdependencies. We train the model end-to-end, and propose an iterative decoding algorithm based on block coordinate descent. Experimental results of English translations from French, German, and Estonian documents show that our model is effective in exploiting both source and target document context, and statistically significantly outperforms the previous work in terms of BLEU and METEOR.
| Original language | English |
|---|---|
| Title of host publication | ACL 2018 - The 56th Annual Meeting of the Association for Computational Linguistics |
| Subtitle of host publication | Proceedings of the Conference, Vol. 1 (Long Papers) |
| Editors | Iryna Gurevych, Yusuke Miyao |
| Place of Publication | Stroudsburg PA USA |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 1275-1284 |
| Number of pages | 10 |
| ISBN (Print) | 9781948087322 |
| Publication status | Published - 2018 |
| Event | Annual Meeting of the Association of Computational Linguistics 2018 - Melbourne, Australia Duration: 15 Jul 2018 → 20 Jul 2018 Conference number: 56th https://aclanthology.info/events/acl-2018 |
Conference
| Conference | Annual Meeting of the Association of Computational Linguistics 2018 |
|---|---|
| Abbreviated title | ACL 2018 |
| Country/Territory | Australia |
| City | Melbourne |
| Period | 15/07/18 → 20/07/18 |
| Internet address |