Using semantic equivalents for Arabic-to-English
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Kfir Bar
Abstract
We explore the effect of using Arabic semantic equivalents in an examplebased Arabic-English translation system. We describe two experiments using single-word equivalents in translation as test cases for broadening the level of similarity and using multi-word Arabic paraphrases in the future. In the first experiment, we used synonymous Arabic nouns, derived from a lexicon, to help locate potential translation examples for fragments of a given input sentence. Not surprisingly, the smaller the parallel corpus, the greater the contribution provided by synonyms. Considering the degree of relevance of the subject matter of a potential match contributes to the quality of the final results. In the second experiment, we used automatically extracted single-word verb paraphrases, derived from a corpus of comparable documents. The experiments were performed within an implementation of a non-structural example-based translation system, using a parallel corpus aligned at the sentence level. The methods developed here should apply to other morphologically-rich languages.
Abstract
We explore the effect of using Arabic semantic equivalents in an examplebased Arabic-English translation system. We describe two experiments using single-word equivalents in translation as test cases for broadening the level of similarity and using multi-word Arabic paraphrases in the future. In the first experiment, we used synonymous Arabic nouns, derived from a lexicon, to help locate potential translation examples for fragments of a given input sentence. Not surprisingly, the smaller the parallel corpus, the greater the contribution provided by synonyms. Considering the degree of relevance of the subject matter of a potential match contributes to the quality of the final results. In the second experiment, we used automatically extracted single-word verb paraphrases, derived from a corpus of comparable documents. The experiments were performed within an implementation of a non-structural example-based translation system, using a parallel corpus aligned at the sentence level. The methods developed here should apply to other morphologically-rich languages.
Chapters in this book
- Prelim pages i
- Table of contents v
- Preface vii
- Introduction 1
- Linguistic resources for Arabic machine translation 15
- Using morphology to improve Example-Based Machine Translation 23
- Using semantic equivalents for Arabic-to-English 49
- Arabic preprocessing for Statistical Machine Translation 73
- Preprocessing for English-to-Arabic Statistical Machine Translation 95
- Lexical syntax for Arabic SMT 109
- Automatic rule induction in Arabic to English machine translation framework 135
- Index 155
Chapters in this book
- Prelim pages i
- Table of contents v
- Preface vii
- Introduction 1
- Linguistic resources for Arabic machine translation 15
- Using morphology to improve Example-Based Machine Translation 23
- Using semantic equivalents for Arabic-to-English 49
- Arabic preprocessing for Statistical Machine Translation 73
- Preprocessing for English-to-Arabic Statistical Machine Translation 95
- Lexical syntax for Arabic SMT 109
- Automatic rule induction in Arabic to English machine translation framework 135
- Index 155
