Phrase Linguistic Classification and Generalization for Improving Statistical Machine Translation
نویسنده
چکیده
In this paper a method to incorporate linguistic information regarding single-word and compound verbs is proposed, as a first step towards an SMT model based on linguistically-classified phrases. By substituting these verb structures by the base form of the head verb, we achieve a better statistical word alignment performance, and are able to better estimate the translation model and generalize to unseen verb forms during translation. Preliminary experiments for the English Spanish language pair are performed, and future research lines are detailed.
منابع مشابه
Improving statistical machine translation with linguistic information
Statistical machine translation (SMT) should benefit from linguistic information to improve performance but current state-of-the-art models rely purely on data-driven models. There are several reasons why prior efforts to build linguistically annotated models have failed or not even been attempted. Firstly, the practical implementation often requires too much work to be cost effective. Where ad...
متن کاملA Generalized Reordering Model for Phrase-Based Statistical Machine Translation
Phrase-based translation models are widely studied in statistical machine translation (SMT). However, the existing phrase-based translation models either can not deal with non-contiguous phrases or reorder phrases only by the rules without an effective reordering model. In this paper, we propose a generalized reordering model (GREM) for phrase-based statistical machine translation, which is not...
متن کاملLearning Bilingual Linguistic Reordering Model for Statistical Machine Translation
In this paper, we propose a method for learning reordering model for BTG-based statistical machine translation (SMT). The model focuses on linguistic features from bilingual phrases. Our method involves extracting reordering examples as well as features such as part-of-speech and word class from aligned parallel sentences. The features are classified with special considerations of phrase length...
متن کاملImprovement of the Results of Statistical Machine Translation System using Anusaaraka
This paper describes an efficient experimental approach for the improvement of translation quality of phrase based statistical machine translation system by utilizing the insights of the rule based machine translation. As the most primitive step it is believed that appending large and accurately designed linguistic resources such as multiword bilingual dictionaries to the existing training corp...
متن کاملLinguistically Annotated BTG for Statistical Machine Translation
Bracketing Transduction Grammar (BTG) is a natural choice for effective integration of desired linguistic knowledge into statistical machine translation (SMT). In this paper, we propose a Linguistically Annotated BTG (LABTG) for SMT. It conveys linguistic knowledge of source-side syntax structures to BTG hierarchical structures through linguistic annotation. From the linguistically annotated da...
متن کامل