In this study, a systematic literature review was conducted to examine the significant works
in the literature on low-resource neural machine translation. Within the scope of the study …

In today's global scenario, frequent international and domestic interactions necessitate the
application of Machine Transliteration and Translation systems to overcome the language …

The language barrier is one of the practical challenges human being face during
communication. To overcome this, researchers are focusing on using machines to translate …

Neural Machine Translation (NMT) heavily depends on the context vectors generated via
attention network for the target word prediction. Existing works primarily focus on generating …

To date, being benefited from the ability of automated feature extraction and the
performance of software vulnerability identification, deep learning techniques have attracted …

Machine Translation has been a field of study for over six decades, but it has acquired
substantial prominence in the last decade as processing capacity in personal computers has …

Names make up a large portion of queries in search engines, while the name ambiguity
problem brings negative effect to the service quality of search engines. In digital academic …

Dogri is a language with low computational resources. There are hardly any digital
resources available for this language. The major hindrance to developing these resources is …

Attention-based neural machine translation (attentional NMT), which jointly aligns and
translates, has got much popularity in recent years. Besides, a language model needs an …

Abstract Named Entity Recognition (NER) is a fundamental task that aids in the completion
of other tasks such as text understanding, information retrieval and question answering in …