A Novel Framework for Neural Machine Translation of Indian-English Languages

Nagarhalli, Tatwadarshi P.; Vaze, Vinod; Rana, N. K.

doi:10.1109/icict48043.2020.9112513

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…The author proposed a conceptual framework by combining document and sentence-level contextual information and an Indian Language-English contextual dictionary fed together with a bi-lingual parallel corpus to the NMT model. The proposed system was expected to address the specific challenges of Indian MT system [29]. As part of the AI4Bharat Initiative, Divyanshu et al2020 developed "IndicBERT," a multilingual pre-trained model based on the compact ALBERT architecture [33].…”

Section: Mahata Et Al 2018 Implemented Rnn Encoder-decodermentioning

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

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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.

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Section: Mahata Et Al 2018 Implemented Rnn Encoder-decodermentioning

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

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show abstract

“…The introduction of approaches, like Neural machine translation, has greatly enhanced its accuracy. There are three techniques under Neural Machine Translation (NMT), Recursive NMT, Recurrent NMT, and Convolution NM [65]. However, more work must be done to improve MT systems' performance, such as Quantum neural networks, a new type of neural network being developed for MT.…”

Section: B Bilingual Evaluation Understudy (Bleu)mentioning

confidence: 99%

A review of existing Machine Translation Approaches, their Challenges and Evaluation Metrics

Ahmed

2023

PJETS

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Machine translation is the process of translating a natural language into another. The primary goal of machine translation is to bridge the linguistic gap between languages. A significant job that may be utilized is to extract information from texts written in different languages through machine translation (MT). Due to the remarkable developments in Machine Translation over the past several years, we have entered the era of Neural Machine Translation (NMT). A review of MT is essential for better understanding of MT in the domain of Natural Language Processing. This paper focuses on Statistical, Corpus, Example, Rule based, Transfer based, direct based and Nural Machine Translation approach, their challenges, and existing evaluation metrics such as BLEU score and Human evaluation metrics.

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The Review of Natural Language Processing Applications with Emphasis on Machine Learning Implementations

Nagarhalli¹,

Mhatre²,

Patil³

et al. 2022

2022 International Conference on Electronics and Renewable Systems (ICEARS)

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A Novel Framework for Neural Machine Translation of Indian-English Languages

Cited by 8 publications

References 12 publications

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

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

A review of existing Machine Translation Approaches, their Challenges and Evaluation Metrics

The Review of Natural Language Processing Applications with Emphasis on Machine Learning Implementations

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