Adapting a Language Model for Controlled Affective Text Generation

Tushar Goswamy, Ishika Singh, Ahsan Barkati, Ashutosh Modi

Abstract
Human use language not just to convey information but also to express their inner feelings and mental states. In this work, we adapt the state-of-the-art language generation models to generate affective (emotional) text. We posit a model capable of generating affect-driven and topic focused sentences without losing grammatical correctness as the affect intensity increases. We propose to incorporate emotion as prior for the probabilistic state-of-the-art text generation model such as GPT-2. The model gives a user the flexibility to control the category and intensity of emotion as well as the topic of the generated text. Previous attempts at modelling fine-grained emotions fall out on grammatical correctness at extreme intensities, but our model is resilient to this and delivers robust results at all intensities. We conduct automated evaluations and human studies to test the performance of our model, and provide a detailed comparison of the results with other models. In all evaluations, our model outperforms existing affective text generation models.
Anthology ID:
2020.coling-main.251
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2787–2801
Language:
URL:
https://aclanthology.org/2020.coling-main.251
DOI:
10.18653/v1/2020.coling-main.251
Bibkey:
Cite (ACL):
Tushar Goswamy, Ishika Singh, Ahsan Barkati, and Ashutosh Modi. 2020. Adapting a Language Model for Controlled Affective Text Generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2787–2801, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Adapting a Language Model for Controlled Affective Text Generation (Goswamy et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.251.pdf
Code
 ishikasingh/Affective-text-gen
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