Publishing Partner: Cambridge University Press CUP Extra Wiley-Blackwell Publisher Login
amazon logo
More Info


New from Oxford University Press!

ad

Words in Time and Place: Exploring Language Through the Historical Thesaurus of the Oxford English Dictionary

By David Crystal

Offers a unique view of the English language and its development, and includes witty commentary and anecdotes along the way.


New from Cambridge University Press!

ad

Thesaurus of English Words and Phrases

By Peter Mark Roget

This book "supplies a vocabulary of English words and idiomatic phrases 'arranged … according to the ideas which they express'. The thesaurus, continually expanded and updated, has always remained in print, but this reissued first edition shows the impressive breadth of Roget's own knowledge and interests."


New from Brill!

ad

The Brill Dictionary of Ancient Greek

By Franco Montanari

Coming soon: The Brill Dictionary of Ancient Greek by Franco Montanari is the most comprehensive dictionary for Ancient Greek to English for the 21st Century. Order your copy now!


Academic Paper


Title: A Weighted Finite State Transducer Translation Template Model for Statistical Machine Translation
Author: Shankar Kumar
Institution: Johns Hopkins University
Linguistic Field: Computational Linguistics; Translation
Abstract: We present a Weighted Finite State Transducer Translation Template Model for statistical machine translation. This is a source-channel model of translation inspired by the Alignment Template translation model. The model attempts to overcome the deficiencies of word-to-word translation models by considering phrases rather than words as units of translation. The approach we describe allows us to implement each constituent distribution of the model as a weighted finite state transducer or acceptor. We show that bitext word alignment and translation under the model can be performed with standard finite state machine operations involving these transducers. One of the benefits of using this framework is that it avoids the need to develop specialized search procedures, even for the generation of lattices or N-Best lists of bitext word alignments and translation hypotheses. We report and analyze bitext word alignment and translation performance on the Hansards French-English task and the FBIS Chinese-English task under the Alignment Error Rate, BLEU, NIST and Word Error-Rate metrics. These experiments identify the contribution of each of the model components to different aspects of alignment and translation performance. We finally discuss translation performance with large bitext training sets on the NIST 2004 Chinese-English and Arabic-English MT tasks.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 12, Issue 1, which you can read on Cambridge's site or on LINGUIST .



Back
Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page