Abstract
Machine Translation (MT) among Indian languages is a challenging problem, owing to multiple factors including their morphological complexity and diversity, in addition to lack of sufficient parallel data for most language pairs. Neural Machine Translation (NMT) is a rapidly advancing MT paradigm and has shown promising results for many language pairs, especially in large training data scenario. We build 110 NMT systems for translation among 11 Indian languages - the first effort in the direction of NMT for Indian languages to the best of our knowledge. Also, since the condition of large parallel corpora is not met for most Indian languages, we propose a method to employ additional linguistic knowledge which is encoded by different phenomena depicted by Indian languages; like Vibhakti, Sandhi and so on. We compare the results obtained on incorporating this knowledge with the baseline systems and demonstrate significant performance improvement. We observe that although NMT models have a strong efficacy to learn language constructs, the usage of specific features further help in improving the performance. To summarize, this paper demonstrates the use of NMT techniques for Indian languages, with an emphasis on the incorporation of specific linguistic knowledge to improve translation quality.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This corpus is available on request from TDIL: https://goo.gl/VHYST.
- 2.
- 3.
The detailed parameters are provided here: http://bit.ly/2xfUj6c.
- 4.
We train our own SMT model since the training, validation and testing sets used by Sata-Anuvadak are unavailable to us.
References
Anthes, G.: Automated translation of Indian languages. Commun. ACM 53(1), 24–26 (2010)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Bentivogli, L., Bisazza, A., Cettolo, M., Federico, M.: Neural versus phrase-based machine translation quality: a case study. arXiv preprint arXiv:1608.04631 (2016)
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
He, W., He, Z., Wu, H., Wang, H.: Improved neural machine translation with SMT features. In: AAAI, pp. 151–157 (2016)
Kalchbrenner, N., Blunsom, P.: Recurrent continuous translation models. In: EMNLP, p. 413, no. 39 (2013)
Klein, G., Kim, Y., Deng, Y., Senellart, J., Rush, A.M.: OpenNMT: open-source toolkit for neural machine translation. ArXiv e-prints (2017)
Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., et al.: Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 177–180. Association for Computational Linguistics (2007)
Kunchukuttan, A., Bhattacharyya, P.: Orthographic syllable as basic unit for SMT between related languages. arXiv preprint arXiv:1610.00634 (2016)
Kunchukuttan, A., Mishra, A., Chatterjee, R., Shah, R., Bhattacharyya, P.: Sataanuvadak: tackling multiway translation of indian languages. pan 841(54,570), 4–135 (2014)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Sig. Process. 45(11), 2673–2681 (1997)
Sennrich, R., Haddow, B.: Linguistic input features improve neural machine translation. arXiv preprint arXiv:1606.02892 (2016)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Werbos, P.J.: Backpropagation through time, what it does and how to do it. In: Proceedings of the IEEE, vol. 78 (1990)
Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Agrawal, R., Shekhar, M., Misra, D. (2017). Integrating Knowledge Encoded by Linguistic Phenomena of Indian Languages with Neural Machine Translation. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-71928-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71927-6
Online ISBN: 978-3-319-71928-3
eBook Packages: Computer ScienceComputer Science (R0)