Name
Abstract | ||
|---|---|---|
This work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a high quality dataset of news articles available in English and Spanish, written for diverse grade levels and propose a method to align segments across comparable bilingual articles. The resulting dataset makes it possible to train multi-task sequence-to-sequence models that translate Spanish into English targeted at an easier reading grade level than the original Spanish. We show that these multi-task models outperform pipeline approaches that translate and simplify text independently. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.18653/v1/D19-1166 | EMNLP/IJCNLP (1) |
DocType | Volume | Citations |
Conference | D19-1 | 1 |
PageRank | References | Authors |
0.36 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sweta Agrawal | 1 | 1 | 2.39 |
| Marine Carpuat | 2 | 587 | 51.99 |