A comprehensive survey on machine translation for English, Hindi and Sanskrit languages

Sitender,; Bawa, Seema; Kumar, M. G. Sharavana; Sangeeta, .

doi:10.1007/s12652-021-03479-0

Cited by 17 publications

(7 citation statements)

References 60 publications

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“…Several studies have assessed the efficacy of various word embedding models spanning diverse linguistic contexts and applications. Investigations have ranged from comparing pre-trained word embedding vectors for word-level semantic text similarity in Turkish [26] to evaluating neural machine translation (NMT) for languages such as English and Hindi [27]. Additionally, the accuracy of three prominent word embedding models within the context of convolutional neural network (CNN) text classification [28] has been explored.…”

Section: Word Embeddingmentioning

confidence: 99%

Improved Evaluation Metrics for Sentence Suggestions in Nursing and Elderly Care Record Applications

Hamdhana,

Kaneko,

Victorino

et al. 2024

Healthcare

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This paper presents a new approach called EmbedHDP, which aims to enhance the evaluation models utilized for assessing sentence suggestions in nursing care record applications. The primary objective is to determine the alignment of the proposed evaluation metric with human evaluators who are caregivers. It is crucial due to the direct relevance of the provided provided to the health or condition of the elderly. The motivation for this proposal arises from challenges observed in previous models. Our analysis examines the mechanisms of current evaluation metrics such as BERTScore, cosine similarity, ROUGE, and BLEU to achieve reliable metrics evaluation. Several limitations were identified. In some cases, BERTScore encountered difficulties in effectively evaluating the nursing care record domain and consistently providing quality assessments of generated sentence suggestions above 60%. Cosine similarity is a widely used method, but it has limitations regarding word order. This can lead to potential misjudgments of semantic differences within similar word sets. Another technique, ROUGE, relies on lexical overlap but tends to ignore semantic accuracy. Additionally, while BLEU is helpful, it may not fully capture semantic coherence in its evaluations. After calculating the correlation coefficient, it was found that EmbedHDP is effective in evaluating nurse care records due to its ability to handle a variety of sentence structures and medical terminology, providing differentiated and contextually relevant assessments. Additionally, this research used a dataset comprising 320 pairs of sentences with correspondingly equivalent lengths. The results revealed that EmbedHDP outperformed other evaluation models, achieving a coefficient score of 61%, followed by cosine similarity, with a score of 59%, and BERTScore, with 58%. This shows the effectiveness of our proposed approach in improving the evaluation of sentence suggestions in nursing care record applications.

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Section: Word Embeddingmentioning

confidence: 99%

Improved Evaluation Metrics for Sentence Suggestions in Nursing and Elderly Care Record Applications

Hamdhana,

Kaneko,

Victorino

et al. 2024

Healthcare

View full text Add to dashboard Cite

show abstract

“…(1) S = Decoder(y, H), (2) We choose the Transformer as the foundation of the NMT model because of its superior multilingual performance (Bawa &Kumar, 2021). A self-attention sub-layer and a point-wise feed-forward sub-layer are present in each layer of the encoder's stack of L= 6 identical layers.…”

Section: 𝐇 = Encoder([t X])mentioning

confidence: 99%

A novel approach to multilingual machine translation using hybrid deep learning

Hossain

Zhang

Zheng

et al. 2023

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

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In this chapter we provide the implementation of the proposed model which is based on hybrid BERT model using Asian languages for MNMT. Due to its benefits in streamlining the training process, lowering the cost of online maintenance, and boosting low-resource and zero-shot translation, multilingual neural machine translation (NMT) (Ni et al., 2022), which translates many languages using a single model, is of significant practical significance. It is exceedingly laborious to manage them all in a single model or use a new model for each language pair given that there are thousands of languages in the world, some of which are very diverse. As a result, multilingual NMT relies on the ability to choose which languages should be supported by a single model given a set resource budget, such as the number of models. Unfortunately, this issue has not been addressed by prior work (Singh et al., 2020). In this study, we create a framework for grouping languages into various categories and training a single multilingual model for each category. Moreover, we also provides two language clustering techniques: (1) language family-based language clustering utilizing prior information, and (2) language-based language embedding, where each language is represented by an embedding vector and grouped in the embedding space. In specifically, we train a universal neural machine translation model to extract the embedding vectors of every language(S. . Our studies on 23 languages reveal that the first clustering approach is simple and understandable but results in subpar translation accuracy, whereas the second approach adequately captures the relationship between languages and enhances translation accuracy for almost all languages (Araújo et al., 2020). (Yang et al., 2021).proposed incorporation of many-to-one statistical machine translation is something else we've done (SMT). When compared to the outcome of the standard SMT, this novel technique gave results in terms of translation accuracy that were equivalent (Kituku et al., 2016).

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“…Several studies have been conducted, spanning diverse linguistic contexts and applications, to assess the efficacy of various word embedding models. Investigations have ranged from comparing pre-trained word embedding vectors for word-level semantic text similarity in Turkish [26] to evaluating Neural Machine Translation (NMT) for languages such as English and Hindi [27]. Additionally, an exploration of the accuracy of three prominent word embedding models within the context of Convolutional Neural Network (CNN) text classification [28] has been undertaken.…”

Section: Word Embeddingmentioning

confidence: 99%

An Improved Evaluation Metrics for Sentence Suggestions in Nursing and Elderly Care Record Applications

Hamdhana,

Kaneko,

Victorino

et al. 2023

Preprint

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In this paper, we propose a novel approach named EmbedHDP to enhance the evaluation models used to assess sentence suggestions within nursing care record applications. The key focus is determining whether these suggestions garner assessments that align with caregivers as human evaluators. It is crucial due to the direct relevance of the information provided to the health or condition of the elderly. The motivation behind this proposal stems from challenges observed in previous models, such as BERTScore, which encountered difficulties in effectively evaluating the nurse care record domain, consistently providing quality assessments of generated sentence suggestions above 60%. Additionally, while widely used, cosine similarity exhibits limitations concerning word order, leading to potential misjudgments of semantical differences within similar word sets. Similarly, relying on lexical overlap, ROUGE tends to overlook semantic accuracy. Furthermore, despite its utility, BLEU neglects semantic coherence in its evaluations. EmbedHDP excels in evaluating nurse care records by effectively handling a variety of sentence structures and medical terminology and providing differentiated and contextually relevant assessments. We used a dataset comprising 320 pairs of sentences with correspondingly equivalent lengths. The results revealed that EmbedHDP outperformed other evaluation models, achieving a coefficient score of 61%, followed by cosine similarity with a score of 59%, and BERTScore with 58%. This shows the effectiveness of our proposed approach in improving the evaluation of sentence suggestions in nursing care record applications.

show abstract

A comprehensive survey on machine translation for English, Hindi and Sanskrit languages

Cited by 17 publications

References 60 publications

Improved Evaluation Metrics for Sentence Suggestions in Nursing and Elderly Care Record Applications

Improved Evaluation Metrics for Sentence Suggestions in Nursing and Elderly Care Record Applications

A novel approach to multilingual machine translation using hybrid deep learning

An Improved Evaluation Metrics for Sentence Suggestions in Nursing and Elderly Care Record Applications

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