A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages
We propose a language-independent approach for improving statistical machine translation for morphologically rich languages using a hybrid morpheme-word representation where the basic unit of translation is the morpheme, but word boundaries are respected at all stages of the translation process. Our model extends the classic phrase-based model by means of (1) word boundary-aware morpheme-level phrase extraction, (2) minimum error-rate training for a morpheme-level translation model using word-level BLEU, and (3) joint scoring with morpheme- and word-level language models. Further improvements are achieved by combining our model with the classic one. The evaluation on English to Finnish using Europarl (714K sentence pairs; 15.5M English words) shows statistically significant improvements over the classic model based on BLEU and human judgments.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationSentenceTranslationSimilar Papers 제목 키워드 기반
Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation. In contrast to most modern neural systems of translation, whi…
Hard AttentionMachine TranslationTranslationAmharic-English Speech Translation in Tourism Domain
This paper describes speech translation from Amharic-to-English, particularly Automatic Speech Recognition (ASR) with post-editing feature and Amharic-English Statistical Machine Translation (SMT). ASR experiment is cond…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Language ModelingLanguage Modelling+4Simultaneous Word-Morpheme Alignment for Statistical Machine Translation
Uzbek-English and Turkish-English Morpheme Alignment Corpora
Morphologically-rich languages pose problems for machine translation (MT) systems, including word-alignment errors, data sparsity and multiple affixes. Current alignment models at word-level do not distinguish words and …
Machine TranslationTranslationWord AlignmentMoVoC: Morphology-Aware Subword Construction for Geez Script Languages
Subword-based tokenization methods often fail to preserve morphological boundaries, a limitation especially pronounced in low-resource, morphologically complex languages such as those written in the Geez script. To addre…