Bilingual Correspondence Recursive Autoencoders for Statistical Machine Translation
Jinsong Su, Deyi Xiong, Biao Zhang, Yang Liu, Junfeng Yao, Min Zhang · 2015
Learning semantic representations and tree structures of bilingual phrases is ben-eficial for statistical machine translation. In this paper, we propose a new neu-ral network model called Bilingual Corre-spondence Recursive Autoencoder (BCor-rRAE) to model bilingual phrases in trans-lation. We incorporate word alignments into BCorrRAE to allow it freely ac-cess bilingual constraints at different lev-els. BCorrRAE minimizes a joint objec-tive on the combination of a recursive au-toencoder reconstruction error, a structural alignment consistency error and a cross-lingual reconstruction error so as to not only generate alignment-consistent phrase structures, but also capture different lev-els of semantic relations within bilingual phrases. In order to examine the effective-ness of BCorrRAE, we incorporate both semantic and structural similarity features built on bilingual phrase representations and tree structures learned by BCorrRAE into a state-of-the-art SMT system. Exper-iments on NIST Chinese-English test sets show that our model achieves a substantial improvement of up to 1.55 BLEU points over the baseline. 1