Correction of Errors in a Modality Corpus Used for Machine Translation by Using Machine-learning Method

TL;DR

This paper presents a machine-learning approach to correct errors in a modality corpus, improving its quality for machine translation applications.

Contribution

It introduces a novel corpus correction method using maximum-entropy models and compares various techniques to identify the most effective approach.

Findings

Maximum-entropy method effectively corrects corpus errors.

The corrected corpus enhances machine translation quality.

Developed a superior corpus correction technique.

Abstract

We performed corpus correction on a modality corpus for machine translation by using such machine-learning methods as the maximum-entropy method. We thus constructed a high-quality modality corpus based on corpus correction. We compared several kinds of methods for corpus correction in our experiments and developed a good method for corpus correction.