Y. Ajjour, W. Chen, J. Kiesel, H. Wachsmuth, and B. Stein, Unit segmentation of argumentative texts, Proceedings of the 4th Workshop on Argument Mining, pp.118-128, 2017.

A. Héctor-martínez and B. Plank, When is multitask learning effective? semantic sequence prediction under varying data conditions, 2016.

D. Bahdanau, K. Cho, and Y. Bengio, Neural machine translation by jointly learning to align and translate, 2014.

J. Bingel and A. Søgaard, Identifying beneficial task relations for multi-task learning in deep neural networks, 2017.

E. Cabrio and S. Villata, Five years of argument mining: a data-driven analysis, IJCAI, pp.5427-5433, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01876495

K. Cho, B. Van-merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares et al., Learning phrase representations using rnn encoder-decoder for statistical machine translation, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01433235

J. Eckle-kohler, R. Kluge, and I. Gurevych, On the role of discourse markers for discriminating claims and premises in argumentative discourse, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.2236-2242, 2015.

S. Eger, J. Daxenberger, and I. Gurevych, Neural end-to-end learning for computational argumentation mining, 2017.

T. Goudas and C. Louizos, Argument extraction from news, blogs, and social media, Hellenic Conference on Artificial Intelligence, pp.287-299, 2014.

K. Hashimoto, C. Xiong, Y. Tsuruoka, and R. Socher, A joint many-task model: Growing a neural network for multiple nlp tasks, 2016.

K. He, X. Zhang, S. Ren, and J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, Proceedings of the IEEE international conference on computer vision, pp.1026-1034, 2015.

P. Diederik, J. Kingma, and . Ba, Adam: A method for stochastic optimization, 2014.

R. Levy, Y. Bilu, D. Hershcovich, E. Aharoni, and N. Slonim, Context dependent claim detection, Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp.1489-1500, 2014.

M. Lippi and P. Torroni, Argumentation mining: State of the art and emerging trends, ACM Transactions on Internet Technology (TOIT), vol.16, issue.2, p.10, 2016.

N. Madnani, M. Heilman, J. Tetreault, and M. Chodorow, Identifying high-level organizational elements in argumentative discourse, Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.20-28, 2012.

L. Mou, Z. Meng, R. Yan, G. Li, Y. Xu et al., How transferable are neural networks in nlp applications? arXiv preprint, 2016.

V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th international conference on machine learning (ICML-10), pp.807-814, 2010.

J. Park and C. Cardie, Identifying appropriate support for propositions in online user comments, Proceedings of the first workshop on argumentation mining, pp.29-38, 2014.

R. Pascanu, T. Mikolov, and Y. Bengio, On the difficulty of training recurrent neural networks, International conference on machine learning, pp.1310-1318, 2013.

J. Pennington, R. Socher, and C. Manning, Glove: Global vectors for word representation, Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp.1532-1543, 2014.

I. Persing and V. Ng, End-to-end argumentation mining in student essays, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.1384-1394, 2016.

P. Potash, A. Romanov, and A. Rumshisky, Here's my point: Joint pointer architecture for argument mining, 2016.

A. Lance, M. Ramshaw, and . Marcus, Text chunking using transformation-based learning, Natural language processing using very large corpora, pp.157-176, 1999.

S. Ruder, An overview of multi-task learning in deep neural networks, 2017.

F. Erik, S. Sang, and . Buchholz, Introduction to the conll-2000 shared task: Chunking. arXiv preprint cs/0009008, 2000.

C. Sardianos, Ioannis Manousos Katakis, Georgios Petasis, and Vangelis Karkaletsis, Proceedings of the 2nd Workshop on Argumentation Mining, pp.56-66, 2015.

M. Andrew, J. L. Saxe, S. Mcclelland, and . Ganguli, Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, 2013.

C. Schulz, S. Eger, J. Daxenberger, T. Kahse, and I. Gurevych, Multitask learning for argumentation mining in lowresource settings, 2018.

A. Søgaard and Y. Goldberg, Deep multi-task learning with low level tasks supervised at lower layers, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol.2, pp.231-235, 2016.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, vol.15, pp.1929-1958, 2014.

C. Stab and I. Gurevych, Parsing argumentation structures in persuasive essays, Computational Linguistics, vol.43, issue.3, pp.619-659, 2017.

Z. Yang, R. Salakhutdinov, and W. Cohen, Multi-task cross-lingual sequence tagging from scratch, 2016.