Abstract
Here the power of a recurrent neural network (RNN) has been exhibited for generating grammatically correct new text from given input text and translation of the new text to the Hindi language with modified bilingual evaluation understudy (BLEU) metric score. Our system aims to generate a grammatically correct new text from given input sentences or paragraphs and translate generated text to Hindi with high translation score. To accomplish a grammatically correct sentence, natural language toolkit (NLTK) is used for grammar correction at the end of text generation. RNN is not very useful for text generation of a gated connection decided to be used for this purpose. The generated text is transferred to machine translation (MT) module. For MT since evaluation is done by humans is a time-consuming task and results differ from evaluator to another evaluator. Hence, the need for assessment of translation system is emerged. The synonym issue is not considered by the BLEU metric. A synonym is treated as a separate word. A modified BLEU (M-BLEU) has been developed as evaluation metrics. It includes several features such as replacing synonym and shallow modules of parsing. The final score of translation is given by BLEU metric scores. Finally, two outputs are there: one is generated text (English) and second is translated text with an improved translation score (Hindi).
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Amin, S.S., Ragha, L. (2021). Text Generation and Enhanced Evaluation of Metric for Machine Translation. In: Jeena Jacob, I., Kolandapalayam Shanmugam, S., Piramuthu, S., Falkowski-Gilski, P. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-8530-2_1
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