Discriminative Reordering Model Adaptation via Structural Learning
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1040-1045.
Abstract
Reordering model adaptation remains a big challenge in statistical machine translation because reordering patterns of translation units often vary dramatically from one domain to another. In this paper, we propose a novel adaptive discriminative reordering model (DRM) based on structural learning, which can capture correspondences among reordering features from two different domains. Exploiting both in-domain and out-of-domain monolingual corpora, our model learns a shared feature representation for cross-domain phrase reordering. Incorporating features of this representation, the DRM trained on out-of-domain corpus generalizes better to in-domain data. Experiment results on the NIST Chinese-English translation task show that our approach significantly outperforms a variety of baselines.
@inproceedings{ijcai2015p151,
title = {Discriminative Reordering Model Adaptation via Structural Learning},
author = {Zhang, Biao and Su, Jinsong and Xiong, Deyi and Duan, Hong and Yao, Junfeng},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {1040--1045},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/151},
}
TY - CPAPER TI - Discriminative Reordering Model Adaptation via Structural Learning AU - Zhang, Biao AU - Su, Jinsong AU - Xiong, Deyi AU - Duan, Hong AU - Yao, Junfeng T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 1040 EP - 1045 UR - https://ijcai.org/proceedings/2015/151 ER -Copied
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