Abstract
String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target-side context. The lack of context is one reason why verb translation recall is as low as 45.5%. We propose a verb lexicon model trained with a feed-forward neural network that predicts the target verb conditioned on a wide source-side context. We show that a syntactic context extracted from the dependency parse of the source sentence improves the model’s accuracy by 1.5% over a baseline trained on a window context. When used as an extra feature for re-ranking the n-best list produced by the string-to-tree MT system, the verb lexicon model improves verb translation recall by more than 7%.- Anthology ID:
- 2016.iwslt-1.11
- Volume:
- Proceedings of the 13th International Conference on Spoken Language Translation
- Month:
- December 8-9
- Year:
- 2016
- Address:
- Seattle, Washington D.C
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- International Workshop on Spoken Language Translation
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2016.iwslt-1.11
- DOI:
- Cite (ACL):
- Maria Nădejde, Alexandra Birch, and Philipp Koehn. 2016. A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation. In Proceedings of the 13th International Conference on Spoken Language Translation, Seattle, Washington D.C. International Workshop on Spoken Language Translation.
- Cite (Informal):
- A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation (Nădejde et al., IWSLT 2016)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2016.iwslt-1.11.pdf