Abstract
Recently, neural machine translation (NMT) has become highly successful achieving state-of-the-art results on many resource-rich language pairs. However, it fails when there is a lack of sufficiently large amount of parallel corpora for a domain and/or language pair. In this paper, we propose an effective method for NMT under a low-resource scenario. The model operates by augmenting the original training data with the examples extracted from the parse trees of the target-side sentences. It provides important evidences to the model as these phrases are relatively smaller and linguistically correct. Our experiment on the benchmark WMT14 dataset shows an improvement of 3.28 BLEU and 3.41 METEOR score for Hindi to English translation. Evaluation on the same language pair with relatively much smaller datasets of judicial and health domains also show the similar trends with significant performance improvement in terms of BLEU (15.63 for judicial and 15.97 for health) and METEOR (14.30 for judicial and 15.93 for health).
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Notes
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Linguistically more accurate as the lengths are short.
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It is based on BLEU score with patience value = 10.
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Acknowledgement
We gratefully acknowledge TDIL, MeitY who supported this research work under development of the project “Hindi to English machine translation for judicial domain”.
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Gupta, K.K., Sen, S., Ekbal, A., Bhattacharyya, P. (2023). Improving Low-Resource NMT with Parser Generated Syntactic Phrases. 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_37
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