Konferensartikel

Will my auxiliary tagging task help? Estimating Auxiliary Tasks Effectivity in Multi-Task Learning

Johannes Bjerva
University of Groningen, The Netherlands

Ladda ner artikel

Ingår i: Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden

Linköping Electronic Conference Proceedings 131:25, s. 216-220

NEALT Proceedings Series 29:25, s. 216-220

Visa mer +

Publicerad: 2017-05-08

ISBN: 978-91-7685-601-7

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

Multitask learning often improves system performance for morphosyntactic and semantic tagging tasks. However, the question of when and why this is the case has yet to be answered satisfactorily. Although previous work has hypothesised that this is linked to the label distributions of the auxiliary task, we argue that this is not sufficient. We show that information-theoretic measures which consider the joint label distributions of the main and auxiliary tasks offer far more explanatory value. Our findings are empirically supported by experiments for morphosyntactic tasks on 39 languages, and are in line with findings in the literature for several semantic tasks.

Nyckelord

Inga nyckelord är tillgängliga

Referenser

Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, and Noah Smith. 2016. Many languages, one parser. Transactions of the Association for Computational Linguistics, 4:431–444.

Johannes Bjerva, Barbara Plank, and Johan Bos. 2016. Semantic tagging with deep residual networks. In Proceedings of COLING 2016, page 35313541, Osaka, Japan.

Rich Caruana. 1998. Multitask learning. Ph.D. thesis, Carnegie Mellon University.

Hao Cheng, Hao Fang, and Mari Ostendorf. 2015. Open-domain name error detection using a multitask rnn. In EMNLP.

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.

Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning, pages 160–167. ACM.

Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(Aug):2493–2537.

Thomas M Cover and Joy A Thomas. 2012. Elements of information theory. John Wiley & Sons.

Jeffrey L Elman. 1990. Finding structure in time. Cognitive science, 14(2):179–211.

Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Networks, 18(5):602–610.

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735–1780.

Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

Héctor Mart´inez Alonso and Barbara Plank. 2016. Multitask learning for semantic sequence prediction under varying data conditions. In arXiv preprint, to appear at EACL 2017 (long paper).

Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Yoav Goldberg, Jan Hajic, Christopher D Manning, Ryan McDonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira, et al. 2016. Universal dependencies v1: A multilingual treebank collection. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016).

Hiroki Ouchi, Kevin Duh, and Yuji Matsumoto. 2014. Improving dependency parsers with supertags. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers, pages 154–158.

Association for Computational Linguistics. Hiroki Ouchi, Kevin Duh, Hiroyuki Shindo, and Yuji Matsumoto. 2016. Transition-Based Dependency Parsing Exploiting Supertags. In IEEE/ACM Transactions on Audio, Speech and Language Processing, volume 24.

Barbara Plank, Anders Søgaard, and Yoav Goldberg. 2016. Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss. In Proceedings of ACL 2016.

Anders Søgaard and Yoav Goldberg. 2016. Deep multi-task learning with low level tasks supervised at lower layers. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, volume 2, pages 231–235. Association for Computational Linguistics.

Anders Søgaard, Anders Johannsen, Barbara Plank, Dirk Hovy, and Hector Martinez. 2014. Whats in a p-value in NLP? In CoNLL-2014.

Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929–1958.

Citeringar i Crossref