Abstract: Neural machine translation (NMT) is a recent and effective technique which led to remarkable improvements in comparison of conventional machine translation techniques. Proposed neural machine translation model developed for the Gujarati language contains encoder-decoder with attention mechanism. In India, almost all the languages are originated from their ancestral language - Sanskrit. They are having inevitable similarities including lexical and named entity similarity. Translating into Indic languages is always be a challenging task. In this paper, we have presented the neural machine translation system (NMT) that can efficiently translate Indic languages like Hindi and Gujarati that together covers more than 58.49 percentage of total speakers in the country. We have compared the performance of our NMT model with automatic evaluation matrices such as BLEU, perplexity and TER matrix. The comparison of our network with Google translate is also presented where it outperformed with a margin of 6 BLEU score on English-Gujarati translation.

Introduction: India is a highly diverse multilingual country in the world. In India, people of different regions use their own regional speaking languages, which makes India a country having world's second highest number of languages. Human spoken languages in India belongs to several language families. Two main of those families are typically known as Indo-Aryan languages having 78.05 percentage Indian speakers BIBREF0 and Dravidian languages having 19.64 BIBREF0 percentage Indian speakers. Hindi and Gujarati are among constitutional languages of India having nearly 601,688,479 BIBREF0 Indian speakers almost 59 BIBREF0 percentage of total country population. Constitute of India under Article 343 offers English as second additional official language having only 226,449 BIBREF0 Indian speakers and nearly 0.02 percentages of total country population BIBREF0. Communication and information exchange among people is necessary for sharing knowledge, feelings, opinions, facts, and thoughts. Variation of English is used globally for human communication. The content available on the Internet is exceptionally dominated by English. Only 20 percent of the world population speaks in English, while in India it is only 0.02 BIBREF0. It is not possible to have a human translator in the country having this much language diversity. In order to bridge this vast language gap we need effective and accurate computational approaches, which require minimum human intervention. This task can be effectively done using machine translation. Machine Translation (MT) is described as a task of computationally translate human spoken or natural language text or speech from one language to another with minimum human intervention. Machine translation aims to generate translations which have the same meaning as a source sentence and grammatically correct in the target language. Initial work on MT started in early 1950s BIBREF1, and has advanced rapidly since the 1990s due to the availability of more computational capacity and training data. Then after, number of approaches have been proposed to achieve more and more accurate machine translation as, Rule-based translation, Knowledge-based translation, Corpus-based translation, Hybrid translation, and Statistical machine translation(SMT) BIBREF1. All the approaches have their own merits and demerits. Among these, SMT which is a subcategory of Corpus based translation, is widely used as it is able to produce better results compared to other previously available techniques. The usage of the Neural networks in machine translation become popular in recent years around the globe and the novel technique of machine translation with the usage of neural network is known as Neural Machine Translation or NMT. In recent years, many works has been carried out on NMT. Little has been done on Indian languages as well BIBREF1. We found the NMT approach on Indic languages is still a challenging task, especially on bilingual machine translation. In our past research work, we have worked on sequence-to-sequence model based machine translation system for Hindi languageBIBREF2. In this work, we have improved that model and applied for English-Gujarati language pair. We have developed a system that uses neural model based on Attention mechanism. Our proposed attention based NMT model is tested with evaluation matrices as BLEU, perplexity and TER. In section 2 overview of related work carried out in the domain of machine translation is described in brief, section 3 gives fundamentals of machine translation process with neural network using attention mechanism, section 4 gives a comparative analysis of various automatic evaluation matrices, section 5 introduce the proposed bilingual neural machine translation models, section 6 shows the implementation and generated results with our attention based NMT model is shown in section 7, conclusion of the paper is presented in section 8.

Question: How are the two different models trained?
Answer: They pre-train the models using 600000 articles as an unsupervised dataset and then fine-tune the models on small training set.