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        <title>Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation</title>
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    <abstract>Multi-aspect controllable text generation aims to control the generated texts in attributes from multiple aspects (e.g., “positive” from sentiment and “sport” from topic). Existing works neglect attribute correlations formed by the intertwining of different attributes. Particularly, the stereotype formed by imbalanced attribute correlations significantly affects multi-aspect control. In this paper, we propose MAGIC, a new multi-aspect controllable text generation method with disentangled counterfactual augmentation. We alleviate the issue of imbalanced attribute correlations during training using counterfactual feature vectors in the attribute latent space by disentanglement. During inference, we enhance attribute correlations by target-guided counterfactual augmentation to further improve multi-aspect control. Experiments show that MAGIC outperforms state-of-the-art baselines in both imbalanced and balanced attribute correlation scenarios.</abstract>
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%0 Conference Proceedings
%T Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation
%A Liu, Yi
%A Liu, Xiangyu
%A Zhu, Xiangrong
%A Hu, Wei
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F liu-etal-2024-multi
%X Multi-aspect controllable text generation aims to control the generated texts in attributes from multiple aspects (e.g., “positive” from sentiment and “sport” from topic). Existing works neglect attribute correlations formed by the intertwining of different attributes. Particularly, the stereotype formed by imbalanced attribute correlations significantly affects multi-aspect control. In this paper, we propose MAGIC, a new multi-aspect controllable text generation method with disentangled counterfactual augmentation. We alleviate the issue of imbalanced attribute correlations during training using counterfactual feature vectors in the attribute latent space by disentanglement. During inference, we enhance attribute correlations by target-guided counterfactual augmentation to further improve multi-aspect control. Experiments show that MAGIC outperforms state-of-the-art baselines in both imbalanced and balanced attribute correlation scenarios.
%U https://aclanthology.org/2024.acl-long.500
%P 9231-9253

Markdown (Informal)

[Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation](https://aclanthology.org/2024.acl-long.500) (Liu et al., ACL 2024)

• Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation (Liu et al., ACL 2024)

ACL

• Yi Liu, Xiangyu Liu, Xiangrong Zhu, and Wei Hu. 2024. Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9231–9253, Bangkok, Thailand. Association for Computational Linguistics.