In order to explore a practical way of improving machine translation (MT) quality, the error types and distribution of MT results have to be analyzed first. This paper analyzed English-Chinese MT errors from the perspective of naming-telling clause (NT clause, hereafter). Two types of text were input to get the MT output: one was to input the whole original English sentences into an MT engine; ...

We focus on two leading state-of-the-art approaches to grammatical error correction – machine learning classification and machine translation. Based on the comparative study of the two learning frameworks and through error analysis of the output of the state-of-the-art systems, we identify key strengths and weaknesses of each of these approaches and demonstrate their complementarity. In particu...

Evaluation of automatic translation output is a difficult task. Several performance measures like Word Error Rate, Position Independent Word Error Rate and the BLEU and NIST scores are widely use and provide a useful tool for comparing different systems and to evaluate improvements within a system. However the interpretation of all of these measures is not at all clear, and the identification o...

In this paper, we explore the use of a statistical machine translation system for optical character recognition (OCR) error correction. We investigate the use of word and character-level models to support a translation from OCR system output to correct french text. Our experiments show that character and word based machine translation correction make significant improvements to the quality of t...

This paper provides an insight into ongoing research focusing on the exploitation of data from learner corpus in order to enhance the performance of an automatic tool aimed at the correction of collocation errors of L2 Spanish speakers. The procedure adopted for collocation annotation is described together with the main difficulties involved in the annotation task, such as the problem of distin...

Translation quality can be evaluated with regard to different aspects, such as accuracy (fidelity), fluency and fitness for purpose. In using a machine translation system for information purposes, accuracy of semantic content is the key aspect of quality. Automated quality metrics developed in the machine translation field have been criticized for conflating fluency of form with accuracy of con...

This paper presents the first study using neural machine translation (NMT) for grammatical error correction (GEC). We propose a twostep approach to handle the rare word problem in NMT, which has been proved to be useful and effective for the GEC task. Our best NMTbased system trained on the CLC outperforms our SMT-based system when testing on the publicly available FCE test set. The same system...