@inproceedings{carbonell-etal-2002-automatic,
title = "Automatic rule learning for resource-limited {MT}",
author = "Carbonell, Jaime and
Probst, Katharina and
Peterson, Erik and
Monson, Christian and
Lavie, Alon and
Brown, Ralf and
Levin, Lori",
editor = "Richardson, Stephen D.",
booktitle = "Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers",
month = oct # " 8-12",
year = "2002",
address = "Tiburon, USA",
publisher = "Springer",
url = "https://aclanthology.org/2002.amta-papers.1/",
pages = "1--10",
abstract = "Machine Translation of minority languages presents unique challenges, including the paucity of bilingual training data and the unavailability of linguistically-trained speakers. This paper focuses on a machine learning approach to transfer-based MT, where data in the form of translations and lexical alignments are elicited from bilingual speakers, and a seeded version-space learning algorithm formulates and refines transfer rules. A rule-generalization lattice is defined based on LFG-style f-structures, permitting generalization operators in the search for the most general rules consistent with the elicited data. The paper presents these methods and illustrates examples."
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<abstract>Machine Translation of minority languages presents unique challenges, including the paucity of bilingual training data and the unavailability of linguistically-trained speakers. This paper focuses on a machine learning approach to transfer-based MT, where data in the form of translations and lexical alignments are elicited from bilingual speakers, and a seeded version-space learning algorithm formulates and refines transfer rules. A rule-generalization lattice is defined based on LFG-style f-structures, permitting generalization operators in the search for the most general rules consistent with the elicited data. The paper presents these methods and illustrates examples.</abstract>
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%0 Conference Proceedings
%T Automatic rule learning for resource-limited MT
%A Carbonell, Jaime
%A Probst, Katharina
%A Peterson, Erik
%A Monson, Christian
%A Lavie, Alon
%A Brown, Ralf
%A Levin, Lori
%Y Richardson, Stephen D.
%S Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers
%D 2002
%8 oct 8 12
%I Springer
%C Tiburon, USA
%F carbonell-etal-2002-automatic
%X Machine Translation of minority languages presents unique challenges, including the paucity of bilingual training data and the unavailability of linguistically-trained speakers. This paper focuses on a machine learning approach to transfer-based MT, where data in the form of translations and lexical alignments are elicited from bilingual speakers, and a seeded version-space learning algorithm formulates and refines transfer rules. A rule-generalization lattice is defined based on LFG-style f-structures, permitting generalization operators in the search for the most general rules consistent with the elicited data. The paper presents these methods and illustrates examples.
%U https://aclanthology.org/2002.amta-papers.1/
%P 1-10
Markdown (Informal)
[Automatic rule learning for resource-limited MT](https://aclanthology.org/2002.amta-papers.1/) (Carbonell et al., AMTA 2002)
ACL
- Jaime Carbonell, Katharina Probst, Erik Peterson, Christian Monson, Alon Lavie, Ralf Brown, and Lori Levin. 2002. Automatic rule learning for resource-limited MT. In Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 1–10, Tiburon, USA. Springer.