Bridging Morpho-Syntactic Gap between Source and Target Sentences for English-Korean Statistical Machine Translation
نویسندگان
چکیده
Often, Statistical Machine Translation (SMT) between English and Korean suffers from null alignment. Previous studies have attempted to resolve this problem by removing unnecessary function words, or by reordering source sentences. However, the removal of function words can cause a serious loss in information. In this paper, we present a possible method of bridging the morpho-syntactic gap for EnglishKorean SMT. In particular, the proposed method tries to transform a source sentence by inserting pseudo words, and by reordering the sentence in such a way that both sentences have a similar length and word order. The proposed method achieves 2.4 increase in BLEU score over baseline phrase-based system.
منابع مشابه
Statistical Machine Translation of English – Manipuri using Morpho-syntactic and Semantic Information
English-Manipuri language pair is one of the rarely investigated with restricted bilingual resources. The development of a factored Statistical Machine Translation (SMT) system between English as source and Manipuri, a morphologically rich language as target is reported. The role of the suffixes and dependency relations on the source side and case markers on the target side are identified as im...
متن کاملAugmenting a Small Parallel Text with Morpho-syntactic Language Resources for Serbian-English Statistical Machine Translation
In this work, we examine the quality of several statistical machine translation systems constructed on a small amount of parallel Serbian-English text. The main bilingual parallel corpus consists of about 3k sentences and 20k running words from an unrestricted domain. The translation systems are built on the full corpus as well as on a reduced corpus containing only 200 parallel sentences. A sm...
متن کاملStatistical machine translation from Slovenian to English
In this paper, we analyse three statistical models for the machine translation of Slovenian into English. All of them are based on the IBM Model 4, but differ in the type of linguistic knowledge they use. Model 4a uses only basic linguistic units of the text, i.e., words and sentences. In Model 4b, lemmatisation is used as a preprocessing step of the translation task. Lemmatisation also makes i...
متن کاملمدل ترجمه عبارت-مرزی با استفاده از برچسبهای کمعمق نحوی
Phrase-boundary model for statistical machine translation labels the rules with classes of boundary words on the target side phrases of training corpus. In this paper, we extend the phrase-boundary model using shallow syntactic labels including POS tags and chunk labels. With the priority of chunk labels, the proposed model names non-terminals with shallow syntactic labels on the boundaries of ...
متن کاملMachine translation: statistical approach with additional linguistic knowledge
In this thesis, three possible aspects of using linguistic (i.e. morpho-syntactic) knowledge for statistical machine translation are described: the treatment of syntactic differences between source and target language using source POS tags, statistical machine translation with a small amount of bilingual training data, and automatic error analysis of translation output. Reorderings in the sourc...
متن کامل