The conventional Mongolian-Chinese statistical machine translation (SMT) model uses Mongolian words and Chinese words to practice the system. However, data sparsity, complex Mongolian morphology and Chinese word segmentation (CWS) errors lead to alignment errors and ambiguities. Some other works use finer-grained Mongolian stems and Chinese characters, which suffer from information loss when in...

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; ...

In this project we combined a statistical machine translation system for the translation of film subtitles from English to Spanish with rule-based grammar checking. At first we trained the best possible statistical machine translation system with the available training data. The largest part of the training corpus consists of freely available amateur subtitles. A smaller part are professionally...

Chinese spell checking is an important component of many NLP applications, including word processors, search engines, and automatic essay rating. Nevertheless, compared to spell checkers for alphabetical languages (e.g., English or French), Chinese spell checkers are more difficult to develop because there are no word boundaries in the Chinese writing system and errors may be caused by various ...

We have assembled a large corpus of student submissions to an automatic grading system, where the subject matter involves the translation of natural language sentences into propositional logic. Of the 2.3 million translation instances in the corpus, 286,000 (approximately 12%) are categorized as being in error. We want to understand the nature of the errors that students make, so that we can de...

In this paper, we evaluate for the first time the use of Machine Translation technology to repair general errors in second language (L2) authoring. Contrary to previously evaluated approaches which rely exclusively on unilingual models of L2, this method takes into account both languages, and is thus able to model linguistic interference phenomena where the author produces an erroneous word for...

We have assembled a large corpus of student submissions to an automatic grading system, where the subject matter involves the translation of natural language sentences into propositional logic. Of the 2.3 million translation instances in the corpus, 286,000 (approximately 12%) are categorized as being in error. We want to understand the nature of the errors that students make, so that we can de...

In this paper we present our attempt to improve machine translation of named entities by using Wikipedia. We recognize named entities based on categories of English Wikipedia articles, extract their potential translations from corresponding Czech articles and incorporate them into a statistical machine translation system as translation options. Our results show a decrease of translation quality...

In this paper, we show that retrieval performance for the ImageCLEF bilingual cross language image retrieval task depends upon, among other factors, the quality of query translation. In this preliminary study, we investigate translation quality and retrieval performance using the Systran machine translation system by comparing a manual assessment of translation adequacy with an automatic score ...