Robust machine translation for multi-domain tasks
type: CreativeWork
description: In this thesis, we investigate and extend the phrase-based approach to statistical machine translation. Due to improved concepts and algorithms, the quality of the generated translation hypotheses has been significantly improved in recent years. Still, the translation quality leaves a lot to be desired when going beyond traditional translation tasks, such as newswire articles, and when addressing more ambitious translation problems. We extend the state-of-the-art in phrase-based translation which enables us to build a robust translation system for multi-domain input. Robustness is hereby regarded as the ability to produce high quality translations for arbitrary input texts, e.g. automatic transcriptions of recognized speech or other unstructured, potentially noisy input. In this work, we focus on Arabic-English translation tasks. We study the search problem for phrase-based statistical machine translation in detail. For this, we examine the effect of the different models on the translation quality. Moreover, we make an explicit distinction between reordering (coverage) and lexical hypotheses in the pruning process and stress the importance of the coverage pruning to adjust the balance between hypotheses representing different reorderings (coverage hypotheses) and hypotheses with different lexical representations. We present constraints to solve the reordering problem in machine translation. To trim our translation system for multi-domain input and to improve the robustness built into the decoder, we apply domain adaptation to the language models and rerank the candidate translations using appropriate rescoring models. We also present our work on adjusting the vocabularies of the speech recognizer and the machine translation system in a preprocessing step and on predicting missing punctuation marks for automatically transcribed speech (in the actual translation process). Processing morphologically rich languages such as Arabic generally poses high demands on preproce…
abstract: In this thesis, we investigate and extend the phrase-based approach to statistical machine translation. Due to improved concepts and algorithms, the quality of the generated translation hypotheses has been significantly improved in recent years. Still, the translation quality leaves a lot to be desired when going beyond traditional translation tasks, such as newswire articles, and when addressing more ambitious translation problems. We extend the state-of-the-art in phrase-based translation which enables us to build a robust translation system for multi-domain input. Robustness is hereby regarded as the ability to produce high quality translations for arbitrary input texts, e.g. automatic transcriptions of recognized speech or other unstructured, potentially noisy input. In this work, we focus on Arabic-English translation tasks. We study the search problem for phrase-based statistical machine translation in detail. For this, we examine the effect of the different models on the translation quality. Moreover, we make an explicit distinction between reordering (coverage) and lexical hypotheses in the pruning process and stress the importance of the coverage pruning to adjust the balance between hypotheses representing different reorderings (coverage hypotheses) and hypotheses with different lexical representations. We present constraints to solve the reordering problem in machine translation. To trim our translation system for multi-domain input and to improve the robustness built into the decoder, we apply domain adaptation to the language models and rerank the candidate translations using appropriate rescoring models. We also present our work on adjusting the vocabularies of the speech recognizer and the machine translation system in a preprocessing step and on predicting missing punctuation marks for automatically transcribed speech (in the actual translation process). Processing morphologically rich languages such as Arabic generally poses high demands on preproce…
author: Bender, Oliver
language: eng
keywords: info:eu-repo/classification/ddc/004, Sprachverarbeitung, Maschinelle Übersetzung, Informatik, natural language processing, machine translation
date published: 2010
date modified: 2026-07-30T19:43:35Z
provider: RWTH Aachen University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Publikationsserver der RWTH Aachen University