Improving statistical machine translation using morpho-syntactic information
type: CreativeWork
description: In the framework of statistical machine translation (SMT), correspondences between the words in the source and the target language are learned from bilingual corpora, and often little or no linguistic knowledge is used to structure the underlying models. The work presented in this thesis is motivated by the observation that training data typically does not sufficiently represent the phenomena in natural languages. Various methods of incorporating morphological and syntactic information into SMT systems are proposed and systematically assessed. The overall goal is to improve translation quality and to reduce the amount of parallel text necessary to train the model parameters. Large differences in word order between corresponding sentences are difficult to capture for automatic alignment algorithms. In this work, sentence level restructuring transformations are introduced which are motivated by the sentence structure in the involved languages. These transformations aim at the assimilation of word orders in related sentences. Their application results in better alignments and as a consequence in cleaner probabilistic lexica, broader applicability of multi-word phrase pairs and a better coverage of the language model. Existing SMT systems often treat different inflected forms of the same lemma as if they were independent of each other. A better exploitation of the bilingual training data can be achieved by taking into account the interdependencies of related inflected forms. In this work a hierarchy of equivalence classes is defined on the basis of morphological and syntactic information. Features from those hierarchy levels are combined to form hierarchical lexicon models which can replace the standard lexicon used in most SMT systems. The benefit from these combined models is twofold: Firstly, the translation of unseen word forms can be derived by considering information from lower levels in the hierarchy. Secondly, category ambiguity can be resolved, because syntacti…
abstract: In the framework of statistical machine translation (SMT), correspondences between the words in the source and the target language are learned from bilingual corpora, and often little or no linguistic knowledge is used to structure the underlying models. The work presented in this thesis is motivated by the observation that training data typically does not sufficiently represent the phenomena in natural languages. Various methods of incorporating morphological and syntactic information into SMT systems are proposed and systematically assessed. The overall goal is to improve translation quality and to reduce the amount of parallel text necessary to train the model parameters. Large differences in word order between corresponding sentences are difficult to capture for automatic alignment algorithms. In this work, sentence level restructuring transformations are introduced which are motivated by the sentence structure in the involved languages. These transformations aim at the assimilation of word orders in related sentences. Their application results in better alignments and as a consequence in cleaner probabilistic lexica, broader applicability of multi-word phrase pairs and a better coverage of the language model. Existing SMT systems often treat different inflected forms of the same lemma as if they were independent of each other. A better exploitation of the bilingual training data can be achieved by taking into account the interdependencies of related inflected forms. In this work a hierarchy of equivalence classes is defined on the basis of morphological and syntactic information. Features from those hierarchy levels are combined to form hierarchical lexicon models which can replace the standard lexicon used in most SMT systems. The benefit from these combined models is twofold: Firstly, the translation of unseen word forms can be derived by considering information from lower levels in the hierarchy. Secondly, category ambiguity can be resolved, because syntacti…
author: Nießen, Sonja
language: eng
keywords: info:eu-repo/classification/ddc/004, Informatik, statistical machine translation, morphological and syntactic information, quality evaluation, machine learning, maximum entropy
date published: 2002
date modified: 2026-07-30T19:42:31Z
provider: RWTH Aachen University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Publikationsserver der RWTH Aachen University