Rule-based Syntactic Preprocessing for Syntax-based Machine Translation
Yuto Hatakoshi,Graham Neubig,Sakriani Sakti,Tomoki Toda,Satoshi Nakamura +4 more
- 01 Oct 2014
- pp 34-42
TL;DR: This paper tailor a highly successful preprocessing method for EnglishJapanese PBMT to syntax-based SMT, and finds that while the gains achievable are smaller than those for P BMT, significant improvements in accuracy can be realized.
read more
Abstract: Several preprocessing techniques using syntactic information and linguistically motivated rules have been proposed to improve the quality of phrase-based machine translation (PBMT) output. On the other hand, there has been little work on similar techniques in the context of other translation formalisms such as syntax-based SMT. In this paper, we examine whether the sort of rule-based syntactic preprocessing approaches that have proved beneficial for PBMT can contribute to syntax-based SMT. Specifically, we tailor a highly successful preprocessing method for EnglishJapanese PBMT to syntax-based SMT, andfind that while the gains achievable are smaller than those for PBMT, significant improvements in accuracy can be realized.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Hybrid Machine Translation Overview
Cristina España-Bonet,Marta R. Costa-jussà +1 more
- 01 Jan 2016
TL;DR: This survey chapter provides an overview of the recent research in hybrid Machine Translation (MT) starting with system combination techniques and followed by integration strategies led by rule-based and statistical systems.
8
•Posted Content
A Comprehensive Survey on Aspect Based Sentiment Analysis
TL;DR: The aim is to explore the various methodologies practiced while performing ABSA, and provide a comparative study, discussing various solutions in-depth and giving a comparison between them.
References
Bleu: a Method for Automatic Evaluation of Machine Translation
Kishore Papineni,Salim Roukos,Todd Ward,Wei-Jing Zhu +3 more
- 06 Jul 2002
TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
Moses: Open Source Toolkit for Statistical Machine Translation
Philipp Koehn,Hieu Hoang,Alexandra Birch,Chris Callison-Burch,Marcello Federico,Nicola Bertoldi,Brooke Cowan,Wade Shen,C. Corbett Moran,Richard Zens,Chris Dyer,Ondrej Bojar,Alexandra Elena Constantin,Evan Herbst +13 more
- 25 Jun 2007
TL;DR: An open-source toolkit for statistical machine translation whose novel contributions are support for linguistically motivated factors, confusion network decoding, and efficient data formats for translation models and language models.
A systematic comparison of various statistical alignment models
Franz Josef Och,Hermann Ney +1 more
TL;DR: An important result is that refined alignment models with a first-order dependence and a fertility model yield significantly better results than simple heuristic models.
Statistical phrase-based translation
Philipp Koehn,Franz Josef Och,Daniel Marcu +2 more
- 27 May 2003
TL;DR: The empirical results suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translation.
Minimum Error Rate Training in Statistical Machine Translation
Franz Josef Och
- 07 Jul 2003
TL;DR: It is shown that significantly better results can often be obtained if the final evaluation criterion is taken directly into account as part of the training procedure.
3.4K