SANSUNL: A Sanskrit to UNL Enconverter System

Sitender,; Bawa, Seema

doi:10.1080/03772063.2018.1528187

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…There are a few systems of machine translation based on UNL, such as Saravanan's Tamil enconverter [9], Kumar's Sanskrit enconverter [28] or Geetha's Tamil deconverter [8] and the Shi and Chen's Chinese deconverter [26].…”

Section: State Of the Artmentioning

confidence: 99%

Syntactic Quality Measurement in Machine Translation with Interlinguas

Maisto,

Oliver

2023

Proceedings of the International Conference on Human-Informed Translation and Interpreting Technology 2023

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The measurement of machine translation (MT) performances is an unsolved issue in NLP. This task can be done by a human but the time cost and the need for skilled workers to do it rise the necessity for automatic ways to measure the quality of a translation. In this work, we aim to develop a new methodology for measuring the quality of MT results from a syntactic point of view. The idea takes as a theoretical framework the work of Harris about the decomposition of sentences in elementary units called kernels. Our model parses Spanish sentences and UNL (Universal Networking Language) Graphs with a rule-based methodology and divides them into units of information.Comparing those units the model measures the quality of the translation. Our results show that decomposing the sentences in minimal syntactic units could improve the evaluation performances also without a lexical/semantic analysis.

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Section: State Of the Artmentioning

confidence: 99%

Syntactic Quality Measurement in Machine Translation with Interlinguas

Maisto,

Oliver

2023

Proceedings of the International Conference on Human-Informed Translation and Interpreting Technology 2023

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“…CNN is popular for its performance in image classification and therefore has a vast span of applications in various fields [29] such as emotion detection [30], the medical field, the agriculture field [2], Natural Language Translation [31][32][33] and sign language translation. The CNN model is a feedforward artificial neural network used in image recognition tasks because of its optimal performance.…”

Section: Convolution Neural Networkmentioning

confidence: 99%

Alphabet-Level Indian Sign Language Translation to Text Using Hybrid-AO Thresholding with CNN

Sabharwal,

Singla

2023

Intelligent Automation &Amp; Soft Computing

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Sign language is used as a communication medium in the field of trade, defence, and in deaf-mute communities worldwide. Over the last few decades, research in the domain of translation of sign language has grown and become more challenging. This necessitates the development of a Sign Language Translation System (SLTS) to provide effective communication in different research domains. In this paper, novel Hybrid Adaptive Gaussian Thresholding with Otsu Algorithm (Hybrid-AO) for image segmentation is proposed for the translation of alphabet-level Indian Sign Language (ISLTS) with a 5-layer Convolution Neural Network (CNN). The focus of this paper is to analyze various image segmentation (Canny Edge Detection, Simple Thresholding, and Hybrid-AO), pooling approaches (Max, Average, and Global Average Pooling), and activation functions (ReLU, Leaky ReLU, and ELU). 5-layer CNN with Max pooling, Leaky ReLU activation function, and Hybrid-AO (5MXLR-HAO) have outperformed other frameworks. An open-access dataset of ISL alphabets with approx. 31 K images of 26 classes have been used to train and test the model. The proposed framework has been developed for translating alphabet-level Indian Sign Language into text. The proposed framework attains 98.95% training accuracy, 98.05% validation accuracy, and 0.0721 training loss and 0.1021 validation loss and the performance of the proposed system outperforms other existing systems.

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“…The training purpose of the NMT system is the same as that of a recurrent language model, with the exception that the target sentence is conditional on the source sentence. For neural machine translation training over the parallel corpus T, the loss function is (8).…”

Section: Trainingmentioning

confidence: 99%

“…Sanskrit is a language of the world's greatest study. It was evaluated both in India and overseas in many grammar schools [7], [8]. The number of grammatical categories in Sanskrit is traditionally varied between one and five.…”

Section: Introductionmentioning

confidence: 99%

Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation

Chingamtotattil

Gopikakumar

2022

IJEECS

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Neural machine translation (NMT) is a fast-evolving MT paradigm and showed good results, particularly in large training data circumstances, for several language pairs. In this paper, we have utilized Sanskrit to Malayalam language pair neural machines translation. The attention-based mechanism for the development of the machine translation system was particularly exploited. Word sense disambiguation (WSD) is a phenomenon for disambiguating the text to let the machine infer the proper definition of the particular word. Sequential deep learning approaches such as a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short term memory (LSTM), and a bi-directional LSTM (BLSTM) were used to analyze the tagged data. By adding morphological elements and evolutionary word sense disambiguation, the suggested common character-word embedding-based NMT model gives a BLEU score of 38.58 which was higher than the others.

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SANSUNL: A Sanskrit to UNL Enconverter System

Cited by 11 publications

References 5 publications

Syntactic Quality Measurement in Machine Translation with Interlinguas

Syntactic Quality Measurement in Machine Translation with Interlinguas

Alphabet-Level Indian Sign Language Translation to Text Using Hybrid-AO Thresholding with CNN

Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation

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