A Comparison of Character-Based Neural Machine Translations Techniques Applied to Spelling Normalization

Author:

Domingo Miguel,Casacuberta Francisco

Publisher

Springer International Publishing

Reference46 articles.

1. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2015)

2. Baron, A., Rayson, P.: VARD2: a tool for dealing with spelling variation in historical corpora. In: Postgraduate Conference in Corpus Linguistics (2008)

3. Bollmann, M.: Normalization of historical texts with neural network models. Ph.D. thesis, Sprachwissenschaftliches Institut, Ruhr-Universität (2018)

4. Bollmann, M., Søgaard, A.: Improving historical spelling normalization with bi-directional LSTMs and multi-task learning. In: Proceedings of the International Conference on the Computational Linguistics, pp. 131–139 (2016)

5. Brown, P.F., Pietra, V.J.D., Pietra, S.A.D., Mercer, R.L.: The mathematics of statistical machine translation: parameter estimation. Comput. Linguist. 19(2), 263–311 (1993)

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