Abstract
Due to the ever-changing nature of the human language and the variations in writing style, age-old texts in one language may be incomprehensible to a modern reader. In order to make these texts familiar to the modern reader, we need to rewrite them manually. But this is not always feasible if the volume of texts is very large. In this paper, we present this rewriting task as a neural machine translation (NMT) problem. We propose an effective approach for training NMT system using a tiny parallel corpus comprising of only 2.7 k parallel sentences. We inject parallel phrase pairs extracted using Statistical Machine Translation (SMT) as additional training examples to NMT. We choose publicly available old-modern English parallel texts for our experiments. Evaluation results show that our proposed approach outperforms the baseline NMT system by more than 18 BLEU points without using any additional training data.
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Notes
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We use early stopping based on BLEU measure with early-stopping patience value 10. All the models run for 110–140 k (approx.) updates before early-stopping.
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Acknowledgments
Asif Ekbal acknowledges Young Faculty Research Fellowship (YFRF), supported by Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia). Mohammed Hasanuzzaman and Andy Way would like to acknowledge ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
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Sen, S., Hasanuzzaman, M., Ekbal, A., Bhattacharyya, P., Way, A. (2023). Take Help from Elder Brother: Old to Modern English NMT with Phrase Pair Feedback. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_39
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