Quantum neural network based machine translator for English to Hindi

Narayan, Ravi; Chakraverty, Snehashish; Singh, Varinder

doi:10.1016/j.asoc.2015.08.031

“…These ambiguities forced the authors to explore deep learning (Costa-jussà et al, 2017) for the English-Hindi translation system. Narayan et al (2016) presented a quantum neural network machine translator based on the concept of machine learning of semantically correct corpus. The system performed the translation task using its knowledge gained while learning the input sentences from source to target language.…”

Section: Related Workmentioning

confidence: 99%

Context-based Machine Translation of English-Hindi using CE-Encoder

Bansal¹,

Lobiyal²

2021

Journal of Computer Science

0

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The difficulty in obtaining accurate word alignment and determining a target word that is the best candidate for a source context in machine translation leads to different translations. In this study, we propose a method with a more accurate context model. Our Neural Machine Translation (NMT) approach focuses on the encoder to apprehend the meaning of source sentences for improved translation. The recurrent encoder works by taking into consideration the history and future information of the source context. In this study, we implement the proposed approach into three steps. Firstly, we learn the representation of future context in advance. Secondly, a context-based recurrent encoder called as CE-Encoder with two-level Gated Recurrent Unit (GRU) is used. In this, the bottom-level GRU gathers history data of a sentence and top-level GRU assembles future data information. Finally, the future learned context and the history information from the opposite direction is integrated. The distinguishing factor of the proposed framework from the existing models, specifically Bidirectional Recurrent Neural Network (BiRNN) is that, the current models have not spent substantial time and capacity in learning future context or disambiguating source and target words based on the context which is defined by source sentence. We conduct experiments on the datasets from ILCC and CFILT for the English-Hindi language pair. From the comparative evaluation, we observed that the proposed model outperforms the Bidirectional RNN encoder in terms of translation quality. The proposed model has shown the improvement of 7 Bleu points using the ILCC dataset and 9 points using the CFILT dataset over BiRNN.

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“…These ambiguities forced the authors to explore deep learning (Costajussà et al, 2017) for the English-Hindi translation system. Narayan et al (2016) presented a quantum neural network machine translator based on the concept of machine learning of semantically correct corpus. The system performed the translation task using its knowledge gained while learning the input sentences from source to target language.…”

Section: Related Workmentioning

confidence: 99%

Context-based Machine Translation of English-Hindi using CE-Encoder

Bansal¹,

Lobiyal²

2021

Journal of Computer Science

0

View full text Add to dashboard Cite

The difficulty in obtaining accurate word alignment and determining a target word that is the best candidate for a source context in machine translation leads to different translations. In this study, we propose a method with a more accurate context model. Our Neural Machine Translation (NMT) approach focuses on the encoder to apprehend the meaning of source sentences for improved translation. The recurrent encoder works by taking into consideration the history and future information of the source context. In this study, we implement the proposed approach into three steps. Firstly, we learn the representation of future context in advance. Secondly, a context-based recurrent encoder called as CE-Encoder with two-level Gated Recurrent Unit (GRU) is used. In this, the bottom-level GRU gathers history data of a sentence and top-level GRU assembles future data information. Finally, the future learned context and the history information from the opposite direction is integrated. The distinguishing factor of the proposed framework from the existing models, specifically Bidirectional Recurrent Neural Network (BiRNN) is that, the current models have not spent substantial time and capacity in learning future context or disambiguating source and target words based on the context which is defined by source sentence. We conduct experiments on the datasets from ILCC and CFILT for the English-Hindi language pair. From the comparative evaluation, we observed that the proposed model outperforms the Bidirectional RNN encoder in terms of translation quality. The proposed model has shown the improvement of 7 Bleu points using the ILCC dataset and 9 points using the CFILT dataset over BiRNN.

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“…There are several approaches to the concept of quantum machine translation in the academic literature. For instance, one study posits the implementation of a quantum neural network-based machine translator that is specifically designed for English language translations [36]. Another scholarly article serves as an introductory guide to the field of quantum machine learning, which has significant implications for applications such as image and speech recognition or strategic optimisation [12,37].…”

Section: Introductionmentioning

confidence: 99%

Application of Quantum Natural Language Processing for Language Translation

Abbaszade

¹

,

Salari

²

,

Mousavi

³

et al. 2021

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In this paper, we develop compositional vector-based semantics of positive transitive sentences using quantum natural language processing (Q-NLP) to compare the parametrized quantum circuits of two synonymous simple sentences in English and Persian. We propose a protocol based on quantum long short-term memory (Q-LSTM) for Q-NLP to perform various tasks in general but specifically for translating a sentence from English to Persian. Then, we generalize our method to use quantum circuits of sentences as an input for the Q-LSTM cell. This enables us to translate sentences in different languages.Our work paves the way toward representing quantum neural machine translation, which may demonstrate quadratic speedup and converge faster or reaches a better accuracy over classical methods.

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Quantum neural network based machine translator for English to Hindi

Cited by 14 publications

References 27 publications

Context-based Machine Translation of English-Hindi using CE-Encoder

Context-based Machine Translation of English-Hindi using CE-Encoder

Context-based Machine Translation of English-Hindi using CE-Encoder

Application of Quantum Natural Language Processing for Language Translation

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