Neural Machine Translation Models using Binarized Prediction and Error Correction
نویسندگان
چکیده
منابع مشابه
Grammatical error correction using neural machine translation
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...
متن کاملOCR Error Correction Using Statistical Machine Translation
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...
متن کاملA Binarized Neural Network Joint Model for Machine Translation
The neural network joint model (NNJM), which augments the neural network language model (NNLM) with an m-word source context window, has achieved large gains in machine translation accuracy, but also has problems with high normalization cost when using large vocabularies. Training the NNJM with noise-contrastive estimation (NCE), instead of standard maximum likelihood estimation (MLE), can redu...
متن کاملGrammatical Error Correction: Machine Translation and Classifiers
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...
متن کاملNeural Network Translation Models for Grammatical Error Correction
Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn text transformations from erroneous to corrected text, without explicitly modeling error types. However, phrase-based SMT systems suffer from limitations of d...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Natural Language Processing
سال: 2018
ISSN: 1340-7619,2185-8314
DOI: 10.5715/jnlp.25.167