In this paper we describe and compare two techniques for the automatic diacritization of Arabic text: First, we treat diacritization as a monotone machine translation problem, proposing and evaluating several translation and language models, including word and character-based models separately and combined as well as a model which uses statistical machine translation (SMT) to post-edit a rule-b...

This paper describes our ongoing work on grammatical error correction (GEC). Focusing on all possible error types in a real-life environment, we propose a factored statistical machine translation (SMT) model for this task. We consider error correction as a series of language translation problems guided by various linguistic information, as factors that influence translation results. Factors inc...

This work gives a brief overview of information theory based approaches to genetic sequence and system analysis. The state of research with regard to error-correction coding theory methods for evaluating the genetic translation initiation system is explored. We present research results of interest in the area of error-control coding methods for modeling the translation initiation system of Esch...

OCR post-processing is an important step for improving the quality of output texts. Long short-term memory (LSTM) a deep learning model, which has wide-range applications in many domains like time series prediction, natural language processing and speech recognition. In this paper, we propose error correction model using neural machine translation with bidirectional LSTM networks at syllable le...

This paper describes an experiment to evaluate the impact of idioms on Statistical Machine Translation (SMT) process using the language pair English/BrazilianPortuguese. Our results show that on sentences containing idioms a standard SMT system achieves about half the BLEU score of the same system when applied to sentences that do not contain idioms. We also provide a short error analysis and o...