Ghosh, S., Chollet, M. , Laksana, E., Morency, L.-P. and Scherer, S. (2017) Affect-LM: a Neural Language Model for Customizable Affective Text Generation. In: 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, BC, Canada, 30 Jul - 04 Aug 2017, pp. 634-642. ISBN 9781945626753 (doi: 10.18653/v1/P17-1059)
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Publisher's URL: https://doi.org/10.18653/v1/P17-1059
Abstract
Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research effort in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for generation of conversational text, conditioned on affect categories. Our proposed model, Affect-LM enables us to customize the degree of emotional content in generated sentences through an additional design parameter. Perception studies conducted using Amazon Mechanical Turk show that Affect-LM can generate naturally looking emotional sentences without sacrificing grammatical correctness. Affect-LM also learns affect-discriminative word representations, and perplexity experiments show that additional affective information in conversational text can improve language model prediction.
Item Type: | Conference Proceedings |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Chollet, Dr Mathieu |
Authors: | Ghosh, S., Chollet, M., Laksana, E., Morency, L.-P., and Scherer, S. |
College/School: | College of Science and Engineering > School of Computing Science |
ISBN: | 9781945626753 |
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