Abstract
The Hierarchical Sub-Sentential Alignment (HSSA) method is a method to obtain aligned binary tree structures for two aligned sentences in translation correspondence. We propose to use the binary aligned tree structures delivered by this method as training data for preordering prior to machine translation. For that, we learn a Bracketing Transduction Grammar (BTG) from these binary aligned tree structures. In two oracle experiments in English to Japanese and Japanese to English translation, we show that it is theoretically possible to outperform a baseline system with a default distortion limit of 6, by about 2.5 and 5 BLEU points and, 7 and 10 RIBES points respectively, when preordering the source sentences using the learnt preordering model and using a distortion limit of 0. An attempt at learning a preordering model and its results are also reported.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 |
| Publisher | Institute for the Study of Language and Information |
| Pages | 123-132 |
| Number of pages | 10 |
| ISBN (Electronic) | 9788968174285 |
| Publication status | Published - 2016 |
| Event | 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 - Seoul, Korea, Republic of Duration: 2016 Oct 28 → 2016 Oct 30 |
Other
| Other | 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 16/10/28 → 16/10/30 |
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ASJC Scopus subject areas
- Language and Linguistics
- Computer Science (miscellaneous)
- Information Systems
Cite this
HSSA tree structures for BTG-based preordering in machine translation. / Zhang, Yujia; Wang, Hao; Lepage, Yves.
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016. Institute for the Study of Language and Information, 2016. p. 123-132.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - HSSA tree structures for BTG-based preordering in machine translation
AU - Zhang, Yujia
AU - Wang, Hao
AU - Lepage, Yves
PY - 2016
Y1 - 2016
N2 - The Hierarchical Sub-Sentential Alignment (HSSA) method is a method to obtain aligned binary tree structures for two aligned sentences in translation correspondence. We propose to use the binary aligned tree structures delivered by this method as training data for preordering prior to machine translation. For that, we learn a Bracketing Transduction Grammar (BTG) from these binary aligned tree structures. In two oracle experiments in English to Japanese and Japanese to English translation, we show that it is theoretically possible to outperform a baseline system with a default distortion limit of 6, by about 2.5 and 5 BLEU points and, 7 and 10 RIBES points respectively, when preordering the source sentences using the learnt preordering model and using a distortion limit of 0. An attempt at learning a preordering model and its results are also reported.
AB - The Hierarchical Sub-Sentential Alignment (HSSA) method is a method to obtain aligned binary tree structures for two aligned sentences in translation correspondence. We propose to use the binary aligned tree structures delivered by this method as training data for preordering prior to machine translation. For that, we learn a Bracketing Transduction Grammar (BTG) from these binary aligned tree structures. In two oracle experiments in English to Japanese and Japanese to English translation, we show that it is theoretically possible to outperform a baseline system with a default distortion limit of 6, by about 2.5 and 5 BLEU points and, 7 and 10 RIBES points respectively, when preordering the source sentences using the learnt preordering model and using a distortion limit of 0. An attempt at learning a preordering model and its results are also reported.
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M3 - Conference contribution
AN - SCOPUS:85015885084
SP - 123
EP - 132
BT - Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016
PB - Institute for the Study of Language and Information
ER -