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Abstract
String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited targetside 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 feedforward 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%.
We propose a verb lexicon model trained with a feedforward 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%.
Original language | English |
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Title of host publication | Proceedings of the International Workshop on Spoken Language Translation (IWSLT) |
Number of pages | 9 |
Publication status | Published - 1 Dec 2016 |
Event | 13th International Workshop on Spoken Language Translation 2016 - Seattle, United States Duration: 8 Dec 2016 → 9 Dec 2016 https://workshop2016.iwslt.org/ |
Conference
Conference | 13th International Workshop on Spoken Language Translation 2016 |
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Abbreviated title | IWSLT 2016 |
Country/Territory | United States |
City | Seattle |
Period | 8/12/16 → 9/12/16 |
Internet address |
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- 2 Finished
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HimL: Health in my Language
Haddow, B., Birch-Mayne, A. & Webber, B.
1/02/15 → 31/01/18
Project: Research