Source sentence simplification for statistical machine translation

Sentence Simplification by Monolingual Machine Translation

In this paper we describe a method for simplifying sentences using Phrase Based Machine Translation, augmented with a re-ranking heuristic based on dissimilarity, and trained on a monolingual parallel corpus. We compare our system to a word-substitution baseline and two state-of-the-art systems, all trained and tested on paired sentences from the English part of Wikipedia and Simple Wikipedia. ...

Optimizing Statistical Machine Translation for Text Simplification

Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and quantity of manually simplified corpora, which are expensive to build. In this paper, we conduct an indepth adaptation of statistical machine translation to perform text simplification...

Crossing Sentence Boundaries in Statistical Machine Translation

Transformation-based Sentence Splitting method for Statistical Machine Translation

We propose a transformation based sentence splitting method for statistical machine translation. Transformations are expanded to improve machine translation quality after automatically obtained from manually split corpus. Through a series of experiments we show that the transformation based sentence splitting is effective pre-processing to long sentence translation.

Sentence Type Based Reordering Model for Statistical Machine Translation

Many reordering approaches have been proposed for the statistical machine translation (SMT) system. However, the information about the type of source sentence is ignored in the previous works. In this paper, we propose a group of novel reordering models based on the source sentence type for Chinese-toEnglish translation. In our approach, an SVM-based classifier is employed to classify the given...