An improvement in BLEU metric for English-Hindi machine translation evaluation

Malik, Pooja; Baghel, Anurag Singh

doi:10.1109/ccaa.2016.7813740

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Tamil to English Cross Lingual Information Retrieval System for Agricultural Domain Using SVM’ uses one such approach of machine translation where a bilingual dictionary for translating the user queries and n‐gram‐based approach are used to recover the problem of Word Sense Disambiguation (Saravanan & Sathish, 2016). Other language translations from English to Marathi (Salunkhe et al, 2016), English to Hindi (Malik & Baghel, 2016), and English to Arabic (Shaalan et al, 2010) have been carried out for the agricultural domain as well. Marian (Junczys‐Dowmunt et al, 2018) is a self‐contained Neural Machine translation framework developed on C++ to perform machine translation.…”

Section: Related Workmentioning

confidence: 99%

PooRaa‐Agri KG: An agricultural knowledge graph‐based simplified multilingual query system

Sivakumar,

Srinivasan,

Kannan

et al. 2023

Expert Systems

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The current work proposes PooRaa‐Agri KG, an agricultural knowledge graph‐based simplified multilingual query system that works in real time to provide concise answers for agriculture‐based queries. The proposed approach accommodates real‐time and low‐resource queries in English and Hindi with a novel multi‐stage solution consisting of data pre‐processing, sentence simplification, triplet extraction, knowledge graph generation, sentence reconstruction, query‐to‐reconstructed sentence matching, and machine translation as its sub‐modules. In this work, a novel combination of rule‐based sentence simplification and triplet extraction is carried out resulting in a triplet similarity score of 86.56% for the extracted triplets. This method is superior to the existing triplet extraction method whose triplet similarity score was found to be 60.65%. Further, the proposed work makes use of heuristic rules to reconstruct sentences which when evaluated by human evaluators for meaningfulness and grammar resulted in a score of 3.09/4 and 2.95/4 respectively. To complete end‐to‐end communication in the proposed system, a similarity‐based query answer system is proposed in this work.

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Section: Related Workmentioning

confidence: 99%

PooRaa‐Agri KG: An agricultural knowledge graph‐based simplified multilingual query system

Sivakumar,

Srinivasan,

Kannan

et al. 2023

Expert Systems

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“…The configuration in PatchGAN uses Convolution-Batch Norm-Leaky ReLU layers block and the number specifies the number of filters. It is designed with the size of the receptive field, sometimes referred to as the effective receptive field [24][25][26][27][28][29][30][31]. The model output can be a distinct value or a square value activation map predicting about each patch in the input picture is real or fake.…”

Section: Discriminator (Patchgan Architecture)mentioning

confidence: 99%

Conditional Gans as a Solution to Image-to-Image Rendering Problems

Ali¹

2021

ITII

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In many existing solutions of image-to-image rendering problems, the only focus is to find the closest output of the Generative Adversarial Network (GAN). In this research article, authors propose a generative adversarial network, a solution to pixel-to-pixel rendering problems and reduced the loss function to the maximum under all interactions. For achieving the best result, we have considered the mean square loss function in the generator and binary cross for the discriminator. Our proposed model deals with not only images but also read sketches where the edges are not sharp too. We have used a facade dataset to test our proposed model.

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