Introducing Additional Input Information into Interactive Machine Translation Systems

2008 ◽  
pp. 284-295 ◽  
Author(s):  
Germán Sanchis-Trilles ◽  
Maria-Teresa González ◽  
Francisco Casacuberta ◽  
Enrique Vidal ◽  
Jorge Civera
Keyword(s):  
Machine Translation ◽  
Input Information ◽  
Additional Input ◽  
Translation Systems

A first-pass approach for evaluating machine translation systems

1993 ◽  
Vol 8 (1-2) ◽  
pp. 49-58 ◽  
Author(s):  
Pamela W. Jordan ◽  
Bonnie J. Dorr ◽  
John W. Benoit
Keyword(s):  
Machine Translation ◽  
First Pass ◽  
Translation Systems

scholarly journals Interlingua in machine translation systems for sign languages

2014 ◽  
Vol 1 (20) ◽  
pp. 116
Author(s):  
Mikhail Gennadyevich Grif ◽  
Maria Kirillovna Timofeeva
Keyword(s):  
Machine Translation ◽  
Sign Languages ◽  
Translation Systems

scholarly journals NTT’s Machine Translation Systems for WMT19 Robustness Task

2019 ◽  
Author(s):  
Soichiro Murakami ◽  
Makoto Morishita ◽  
Tsutomu Hirao ◽  
Masaaki Nagata
Keyword(s):  
Machine Translation ◽  
Translation Systems

scholarly journals FUNCTIONAL AND PRAGMATIC ADEQUACY OF JOURNALISTIC STYLE TEXTS TRANSLATION APPLYING MACHINE TRANSLATION SYSTEMS

2019 ◽  
Vol 5 ◽  
pp. 48-54 ◽  
Author(s):  
A. G. Goodmanian ◽  
◽  
A. V. Sitko ◽  
I. V. Struk ◽  
◽  
...  
Keyword(s):  
Machine Translation ◽  
Translation Systems

scholarly journals Unsupervised Sub-tree Alignment for Tree-to-Tree Translation

2013 ◽  
Vol 48 ◽  
pp. 733-782 ◽  
Author(s):  
T. Xiao ◽  
J. Zhu
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Experimental Results ◽  
Alignment Accuracy ◽  
Syntactic Structures ◽  
Tree Alignment ◽  
Matrix Encoding ◽  
Word Alignments ◽  
Alignment Model ◽  
Translation Systems

This article presents a probabilistic sub-tree alignment model and its application to tree-to-tree machine translation. Unlike previous work, we do not resort to surface heuristics or expensive annotated data, but instead derive an unsupervised model to infer the syntactic correspondence between two languages. More importantly, the developed model is syntactically-motivated and does not rely on word alignments. As a by-product, our model outputs a sub-tree alignment matrix encoding a large number of diverse alignments between syntactic structures, from which machine translation systems can efficiently extract translation rules that are often filtered out due to the errors in 1-best alignment. Experimental results show that the proposed approach outperforms three state-of-the-art baseline approaches in both alignment accuracy and grammar quality. When applied to machine translation, our approach yields a +1.0 BLEU improvement and a -0.9 TER reduction on the NIST machine translation evaluation corpora. With tree binarization and fuzzy decoding, it even outperforms a state-of-the-art hierarchical phrase-based system.

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