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Jost Gippert: Our Featured Linguist!

"Buenos dias", "buenas noches" -- this was the first words in a foreign language I heard in my life, as a three-year old boy growing up in developing post-war Western Germany, where the first gastarbeiters had arrived from Spain. Fascinated by the strange sounds, I tried to get to know some more languages, the only opportunity being TV courses of English and French -- there was no foreign language education for pre-teen school children in Germany yet in those days. Read more



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What is English? And Why Should We Care?

By: Tim William Machan

To find some answers Tim Machan explores the language's present and past, and looks ahead to its futures among the one and a half billion people who speak it. His search is fascinating and important, for definitions of English have influenced education and law in many countries and helped shape the identities of those who live in them.


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Medical Writing in Early Modern English

Edited by Irma Taavitsainen and Paivi Pahta

This volume provides a new perspective on the evolution of the special language of medicine, based on the electronic corpus of Early Modern English Medical Texts, containing over two million words of medical writing from 1500 to 1700.


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 .



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