A Bilingual Graph-Based Semantic Model for Statistical Machine Translation
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2950-2955.
Abstract
Most existing bilingual embedding methods for Statistical Machine Translation (SMT) suffer from two obvious drawbacks. First, they only focus on simple context such as word count and co-occurrence in document or sliding window to build word embedding, ignoring latent useful information from selected context. Second, word sense but not word form is supposed to be the minimal semantic unit while most existing works are still for word representation. This paper presents Bilingual Graph-based Semantic Model (BGSM) to alleviate such shortcomings. By means of maximum complete sub-graph (clique) for context selection, BGSM is capable of effectively modeling word sense representation instead of the word form itself. The proposed model is applied to phrase pair translation probability estimation and generation for SMT. The empirical results show that BGSM can enhance SMT both in performance (up to +1.3 BLEU) and efficiency in comparison against existing methods.
@inproceedings{ijcai2016p419,
title = {A Bilingual Graph-Based Semantic Model for Statistical Machine Translation},
author = {Wang, Rui and Zhao, Hai and Ploux, Sabine and Lu, Bao-Liang and Utiyama, Masao},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2950--2955},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/419},
}
TY - CPAPER TI - A Bilingual Graph-Based Semantic Model for Statistical Machine Translation AU - Wang, Rui AU - Zhao, Hai AU - Ploux, Sabine AU - Lu, Bao-Liang AU - Utiyama, Masao T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2950 EP - 2955 UR - https://ijcai.org/proceedings/2016/419 ER -Copied
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