Recurrent neural networks (RNNs) have shown clear superiority in sequence modeling, particularly the ones with gated units, such as long short-term memory
In this tutorial, I will first teach you how to build a recurrent neural network (RNN) with a single layer, consisting of one single neuron, with PyTorch and Google Colab. I will also show you how…
In this tutorial I’ll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in a series of seven parts where various aspects and techniques of building…
GloVe + character embeddings + bi-LSTM + CRF for Sequence Tagging (Named Entity Recognition, NER, POS) - NLP example of bidirectionnal RNN and CRF in Tensorflow
B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk. (2015)cite arxiv:1511.06939Comment: Camera ready version (17th February, 2016) Affiliation update (29th March, 2016).
Y. Chen, S. Gilroy, A. Maletti, J. May, and K. Knight. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), page 2261--2271. New Orleans, Louisiana, Association for Computational Linguistics, (June 2018)
L. Arras, A. Osman, K. Müller, and W. Samek. Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, page 113--126. Florence, Italy, Association for Computational Linguistics, (August 2019)
M. Peters, W. Ammar, C. Bhagavatula, and R. Power. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1, page 1756--1765. (2017)
V. Rybalkin, N. Wehn, M. Yousefi, and D. Stricker. Proceedings of the Conference on Design, Automation & Test in Europe, page 1394--1399. European Design and Automation Association, (2017)
Y. Liu, and M. Lapata. (2017)cite arxiv:1705.09207Comment: change to one-based indexing, published in Transactions of the Association for Computational Linguistics (TACL), https://transacl.org/ojs/index.php/tacl/article/view/1185/280.
J. Tan, X. Wan, and J. Xiao. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1, page 1171--1181. (2017)
Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, page 1480--1489. (2016)
Z. Tu, Z. Lu, Y. Liu, X. Liu, and H. Li. (2016)cite arxiv:1601.04811Comment: Add subjective evaluation on top of ACL version: 25% of source words are under-translated by NMT.
A. See, P. Liu, and C. Manning. (2017)cite arxiv:1704.04368Comment: Add METEOR evaluation results, add some citations, fix some equations (what are now equations 1, 8 and 11 were missing a bias term), fix url to pyrouge package, add acknowledgments.
R. Pascanu, T. Mikolov, and Y. Bengio. (2012)cite arxiv:1211.5063Comment: Improved description of the exploding gradient problem and description and analysis of the vanishing gradient problem.
J. Rotsztejn, N. Hollenstein, and C. Zhang. (2018)cite arxiv:1804.02042Comment: Accepted to SemEval 2018 (12th International Workshop on Semantic Evaluation).
M. Miwa, and M. Bansal. (2016)cite arxiv:1601.00770Comment: Accepted for publication at the Association for Computational Linguistics (ACL), 2016. 13 pages, 1 figure, 6 tables.