Machine Translation for Indian Languages Utilizing Recurrent Neural Networks and Attention

Sharma, Sonali; Diwakar, Manoj

doi:10.1007/978-981-19-2281-7_55

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The essence of NMT consists of two key elements: the "encoder" and the "decoder". The input texts are transformed into a context vector (c) by the encoder , and subsequently, the decoder processes this vector to produce single word at a time for the output sentence with a length of m. Unlike other machine translation approaches, NMT requires minimal domain expertise [12]. The encoder-decoder model for NMT can be represented in a block diagram with figure 5.…”

Section: Neural Machine Translation (Nmt)mentioning

confidence: 99%

A Survey on the MT Methods for Indian Languages: MT Challenges, Availability, and Production of Parallel Corpora, Government Policies and Research Directions

Sani,

Vijaya,

V Gangashetty

2024

IJCDS

View full text Add to dashboard Cite

Since 1991, machine translation has been a prominent research area in India, with IIT Kanpur pioneering the original work which has since been expanded to several universities. Only 10 percent of India's 1.3 billion inhabitants can read, write, and speak English with varying degrees of competence, which makes machine translation crucial in overcoming the linguistic barrier to the internet. The Indian market for commercial products and events is greatly influenced by local languages, making the development and translation of region-based content an essential research topic nowadays. However, Indic-to-Indic language direct translation has faced several challenges and is still going through the experimental phase. Several government-sponsored projects are being undertaken in this regard. Still, there are limited sentence-aligned parallel bi-text resources available for the majority of Indian language pairs. This paper presents a detailed survey of the current trends of research on machine translation between Indian languages, along with their challenges over time. It also presents a timeline of recent research conducted and key findings of past surveys conducted over a decade. Under a single canopy, this paper provides sources of data, the progress made in developing datasets for low-resource Indian languages, various models of translation, encouragement from Indian Govt., and finally, new research directions.

show abstract

Section: Neural Machine Translation (Nmt)mentioning

confidence: 99%