Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473.
Bollmann, M. and Søgaard, A. (2016). Improving historical spelling normalization with bi-directional LSTMs and multi-task learning. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. The COLING 2016 Organizing Committee, pp. 131–139.
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar. Association for Computational Linguistics, pp. 1724–1734.
Gesmundo, A. and Samardžić, T. (2012). Lemmatisation as a tagging task. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Jeju Island, Korea: Association for Computational Linguistics, pp. 368–372.
Gulcehre, C., Firat, O., Xu, K., Cho, K., and Bengio, Y. (2016). On integrating a language model into neural machine translation. Computer Speech and Language, 45, 137–148.
Heafield, K. (2011). KenLM: faster and smaller language model queries. In Proceedings of the EMNLP 2011 Sixth Workshop on Statistical Machine Translation, Edinburgh, Scotland, United Kingdom, pp. 187–197.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Comput. 9(8), 1735–1780.
Honnet, P.-E., Popescu-Belis, A., Musat, C., and Baeriswyl, M. (2017). Machine translation of low-resource spoken dialects: Strategies for normalizing Swiss German. ArXiv e-prints, 1710.11035.
Kalchbrenner, N. and Blunsom, P. (2013). Recurrent continuous translation models. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA: Association for Computational Linguistics, pp. 1700–1709.
Koehn, P. and Hoang, H. (2007). Factored translation models. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL).
Luong, M.-T., Pham, H., and Manning, C.D. (2015). Effective approaches to attention-based neural machine translation. In Empirical Methods in Natural Language Processing (EMNLP), Lisbon, Portugal. Association for Computational Linguistics, pp. 1412–1421.
Lusetti, M., Ruzsics, T., Göhring, A., Samardžić, T., and Stark, E. (2018). Encoder-decoder methods for text normalization. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018). Association for Computational Linguistics, pp. 18–28.
Neubig, G., Dyer, C., Goldberg, Y., Matthews, A., Ammar, W., Anastasopoulos, A., Ballesteros, M., Chiang, D., Clothiaux, D., Cohn, T., Duh, K., Faruqui, M., Gan, C., Garrette, D., Ji, Y., Kong, L., Kuncoro, A., Kumar, G., Malaviya, C., Michel, P., Oda, Y., Richardson, M., Saphra, N., Swayamdipta, S., and Yin, P. (2017). Dynet: The dynamic neural network toolkit. arXiv preprint arXiv:1701.03980.
Rash, F. (1998). The German language in Switzerland: Multilingualism, Diglossia and Variation. Lang, Bern.
Ruef, B. and Ueberwasser, S. (2013). The taming of a dialect: Interlinear glossing of Swiss German text messages. In Zampieri, M. and Diwersy, S. (eds), Non-standard Data Sources in Corpus-Based Research, Aachen, Germany, pp. 61–68.
Ruzsics, T. and Samardžić, T. (2017). Neural sequence-to-sequence learning of internal word structure. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), Vancouver, Canada: Association for Computational Linguistics, pp. 184–194.
Samardžić, T., Scherrer, Y., and Glaser, E. (2015). Normalising orthographic and dialectal variants for the automatic processing of Swiss German. In Proceedings of The 4th Biennial Workshop on Less-Resourced Languages. ELRA.
Schmid, H. (1994). Probabilistic part-of-speech tagging using decision trees. In International Conference on New Methods in Language Processing, pages 44–49, Manchester, UK.
Sennrich, R. and Haddow, B. (2016). Linguistic input features improve neural machine translation. In Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers. Association for Computational Linguistics, pp. 83–91.
Stark, E., Ueberwasser, S., and Göhring, A. (2014). Corpus “What’s up, Switzerland?”. Technical report, University of Zurich, Switzerland.
Sutskever, I., Vinyals, O., and Le, Q.V. (2014). Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pp. 3104–3112.
Tjong Kim Sang, E., Bollmann, M., Boschker, R., Casacuberta, F., Dietz, F., Dipper, S., Domingo, M., van der Goot, R., van Koppen, M., Ljubešić, N., Östling, R., Petran, F., Pettersson, E., Scherrer, Y., Schraagen, M., Sevens, L., Tiedemann, J., Vanallemeersch, T., and Zervanou, K. (2017). The CLIN27 shared task: Translating historical text to contemporary language for improving automatic linguistic annotation. Computational Linguistics in the Netherlands Journal 7, 53–64.
Ueberwasser, S. and Stark, E. (2017). What’s up, Switzerland? A corpus-based research project in a multilingual country. Linguistik Online, 84(5), https://doi.org/10.13092/lo.84.3849.