Neural and Statistical Methods for Leveraging Meta-information in Machine Translation

In this paper, we discuss different methods which use meta information and richer context that may accompany source language input to improve machine translation quality. We focus on category information of input text as meta information, but the proposed methods can be extended to all textual and non-textual meta information that might be available for the input text or automatically predicted using the text content. The main novelty of this work is to use state-of-the-art neural network methods to tackle this problem within a statistical machine translation (SMT) framework. We observe translation quality improvements up to 3% in terms of BLEU score in some text categories.

[1]  Prashant Mathur.  Topic adaptation for machine translation of e-commerce content , 2015, MTSUMMIT.

[2]  Hermann Ney,et al.  Investigations on Phrase-based Decoding with Recurrent Neural Network Language and Translation Models , 2015, WMT@EMNLP.

[3]  Kenneth Heafield,et al.  KenLM: Faster and Smaller Language Model Queries , 2011, WMT@EMNLP.

[4]  F ChenStanley,et al.  An Empirical Study of Smoothing Techniques for Language Modeling , 1996, ACL.

[5]  Jordan L. Boyd-Graber,et al.  Models for Dynamic Translation Model Adaptation , 2016 .

[6]  Hermann Ney,et al.  Extending Statistical Machine Translation with Discriminative and Trigger-Based Lexicon Models , 2009, EMNLP.

[7]  Philipp Koehn,et al.  Sparse lexicalised features and topic adaptation for SMT , 2012, IWSLT.

[8]  Alexander M. Fraser,et al.  Target-Side Context for Discriminative Models in Statistical Machine Translation , 2016, ACL.

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

[10]  Marine Carpuat,et al.  Improving Statistical Machine Translation Using Word Sense Disambiguation , 2007, EMNLP.

[11]  Hermann Ney,et al.  Translation Modeling with Bidirectional Recurrent Neural Networks , 2014, EMNLP.

[12]  George F. Foster,et al.  Batch Tuning Strategies for Statistical Machine Translation , 2012, NAACL.

[13]  Philipp Koehn,et al.  Combining Domain and Topic Adaptation for SMT , 2014 .

[14]  Philipp Koehn,et al.  Dynamic Topic Adaptation for Phrase-based MT , 2014, EACL.

[15]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[16]  Wenhu Chen,et al.  Guided Alignment Training for Topic-Aware Neural Machine Translation , 2016, AMTA.

[17]  Ashish Vaswani,et al.  Decoding with Large-Scale Neural Language Models Improves Translation , 2013, EMNLP.

[18]  Evgeny Matusov,et al.  AppTek’s APT machine translation system for IWSLT 2010 , 2010, IWSLT.

[19]  Hermann Ney,et al.  Extended Translation Models in Phrase-based Decoding , 2015, WMT@EMNLP.

[20]  Nadir Durrani,et al.  A Joint Sequence Translation Model with Integrated Reordering , 2011, ACL.

[21]  Ashish Vaswani,et al.  Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies , 2016, HLT-NAACL.

[22]  Ralph Weischedel,et al.  A STUDY OF TRANSLATION ERROR RATE WITH TARGETED HUMAN ANNOTATION , 2005 .

[23]  Richard M. Schwartz,et al.  Fast and Robust Neural Network Joint Models for Statistical Machine Translation , 2014, ACL.

[24]  Yoshua Bengio,et al.  On Using Very Large Target Vocabulary for Neural Machine Translation , 2014, ACL.

[25]  Rico Sennrich,et al.  Improving Neural Machine Translation Models with Monolingual Data , 2015, ACL.

[26]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[27]  Alon Lavie,et al.  Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability , 2011, ACL.

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

[29]  Matthew G. Snover,et al.  A Study of Translation Edit Rate with Targeted Human Annotation , 2006, AMTA.

[30]  Shahram Khadivi,et al.  Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search , 2017, EMNLP.