Recursive Neural Network Based Preordering for English-to-Japanese Machine Translation

The word order between source and target languages significantly influences the translation quality in machine translation. Preordering can effectively address this problem. Previous preordering methods require a manual feature design, making language dependent design costly. In this paper, we propose a preordering method with a recursive neural network that learns features from raw inputs. Experiments show that the proposed method achieves comparable gain in translation quality to the state-of-the-art method but without a manual feature design.

[1]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[2]  M. Utiyama,et al.  A Japanese-English patent parallel corpus , 2007, MTSUMMIT.

[3]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[4]  Katsuhito Sudoh,et al.  Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015 , 2015, WAT.

[5]  Dmitriy Genzel,et al.  Automatically Learning Source-side Reordering Rules for Large Scale Machine Translation , 2010, COLING.

[6]  Chao Wang,et al.  Chinese Syntactic Reordering for Statistical Machine Translation , 2007, EMNLP.

[7]  Philipp Koehn,et al.  Explorer Edinburgh System Description for the 2005 IWSLT Speech Translation Evaluation , 2005 .

[8]  Tetsuji Nakagawa.  Efficient Top-Down BTG Parsing for Machine Translation Preordering , 2015, ACL.

[9]  Taro Watanabe,et al.  Inducing a Discriminative Parser to Optimize Machine Translation Reordering , 2012, EMNLP.

[10]  Alexander M. Fraser,et al.  Determining the placement of German verbs in English-to-German SMT , 2012, EACL.

[11]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[12]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[13]  Philipp Koehn,et al.  Statistical Significance Tests for Machine Translation Evaluation , 2004, EMNLP.

[14]  Masaaki Nagata,et al.  A Clustered Global Phrase Reordering Model for Statistical Machine Translation , 2006, ACL.

[15]  Kevin Duh,et al.  Automatic Evaluation of Translation Quality for Distant Language Pairs , 2010, EMNLP.

[16]  Christoph Tillmann,et al.  A Unigram Orientation Model for Statistical Machine Translation , 2004, NAACL.

[17]  Mark Hopkins,et al.  Source-side Preordering for Translation using Logistic Regression and Depth-first Branch-and-Bound Search , 2014, EACL.

[18]  Franz Josef Och,et al.  Minimum Error Rate Training in Statistical Machine Translation , 2003, ACL.

[19]  Peng Xu,et al.  Using a Dependency Parser to Improve SMT for Subject-Object-Verb Languages , 2009, NAACL.

[20]  Kevin Duh,et al.  Head Finalization: A Simple Reordering Rule for SOV Languages , 2010, WMT@ACL.

[21]  Ji Ma,et al.  Natural Language Processing with Small Feed-Forward Networks , 2017, EMNLP.

[22]  Slav Petrov,et al.  Source-Side Classifier Preordering for Machine Translation , 2013, EMNLP.

[23]  Toshiaki Nakazawa,et al.  ASPEC: Asian Scientific Paper Excerpt Corpus , 2016, LREC.

[24]  Katsuhito Sudoh,et al.  Shift-Reduce Word Reordering for Machine Translation , 2013, EMNLP.

[25]  Masao Utiyama,et al.  Post-Ordering by Parsing with ITG for Japanese-English Statistical Machine Translation , 2013, ACM Trans. Asian Lang. Inf. Process..

[26]  Yusuke Miyao,et al.  Discriminative Preordering Meets Kendall's Tau Maximization , 2015, ACL.

[27]  Fei Xia,et al.  Improving a Statistical MT System with Automatically Learned Rewrite Patterns , 2004, COLING.

[28]  Gonzalo Iglesias,et al.  Fast and Accurate Preordering for SMT using Neural Networks , 2015, HLT-NAACL.

[29]  Masao Utiyama,et al.  Post-ordering by Parsing for Japanese-English Statistical Machine Translation , 2012, ACL.