Error Correcting Parsing for Text-to-text Machine Translation using Finite State Models (1997)
| Venue: | In Proceedings of the Seventh International Conference on Theoretical and Methodological Issues in Machine Translation |
| Citations: | 1 - 1 self |
BibTeX
@INPROCEEDINGS{Amengual97errorcorrecting,
author = {Juan C. Amengual and José M. Benedí Francisco Casacuberta and Asunción Castaño and Antonio Castellanos and David Llorens and Andrés Marzal and Federico Prat and Enrique Vidal and Juan M. Vilar and I Castellón},
title = {Error Correcting Parsing for Text-to-text Machine Translation using Finite State Models},
booktitle = {In Proceedings of the Seventh International Conference on Theoretical and Methodological Issues in Machine Translation},
year = {1997},
pages = {135--142}
}
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Abstract
Abstract. This paper describes the approach followed to perform text-to-text Machine Translation (MT) in the first phase of the European project EUTRANS. This project aims at performing text and speech input MT in limited domain tasks. The EUTRANS system relies on Subsequential Transducers (SSTs), which are finite state translation models that can be automatically learned from training samples. Error Correcting Parsing is employed to increase the robustness of SSTs. After reviewing our approach, this paper presents results with the corpora defined within the EUTRANS project. 1
Citations
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| 2 | Different Approaches for Efficient Error-Correcting Viterbi Parsing: An Experimental Comparison - Amengual, Vidal - 1996 |
| 2 | Evaluating the performance of connected-word speech recognition systems - Hunt - 1988 |
