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Learning to Generate Textual Data

Bouchard, G; Saito Stenetorp, P; Riedel, S; (2016) Learning to Generate Textual Data. In: Su, J and Carreras, X and Duh, K, (eds.) Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. (pp. pp. 1608-1616). Association for Computational Linguistics: Stroudsburg, PA, USA. Green open access

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Abstract

To learn text understanding models with millions of parameters one needs massive amounts of data. In this work, we argue that generating data can compensate for this need. While defining generic data generators is dif- ficult, we propose to allow generators to be “weakly” specified in the sense that a set of parameters controls how the data is generated. Consider for example generators where the example templates, grammar, and/or vocabulary is determined by this set of parameters. Instead of manually tuning these parameters, we learn them from the limited training data at our disposal. To achieve this, we derive an efficient algorithm called GENERE that jointly estimates the parameters of the model and the undetermined generation parameters. We illustrate its benefits by learning to solve math exam questions using a highly parametrized sequence-to-sequence neural network.

Type: Proceedings paper
Title: Learning to Generate Textual Data
Event: EMNLP 2016: Conference on Empirical Methods in Natural Language Processing, 1-5 November 2016, Austin, Texas, USA
Location: Austin, US
Dates: 01 November 2016 - 05 November 2016
ISBN-13: 9781945626258
Open access status: An open access version is available from UCL Discovery
Publisher version: https://aclweb.org/anthology/D/D16/
Language: English
Additional information: Copyright © 2016 Association for Computational Linguistics.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/1530876
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