Owen Cornec, Rahul Nair, et al.
NeurIPS 2021
Large Transformer-based models have shown great performance in sequence-to- sequence tasks such as machine translation, text summarization etc. While these models perform well on the original task they have been trained on, it is hard to use them for a new but related task. We propose CASPer, a framework to perturb the input-output behavior of the original pre-trained sequence-to-sequence model. CASPer learns a perturbation parameter at test time to modify the behavior of pre-trained model and generates samples that have target characteristics. We apply this framework on a pre-trained text summarization model to alter a given input text such that the generated text has a changed sentiment or other attributes. In experiments, we show that CASPer effectively generates controlled text that preserve the original content, are fluent, diverse and follow the steering provided by the attribute model.
Owen Cornec, Rahul Nair, et al.
NeurIPS 2021
Swastik Haldar, Philips George John, et al.
CODS-COMAD 2021
Dhaval Patel, Dzung Phan, et al.
ICDE 2022
George Kour, Samuel Ackerman, et al.
EMNLP 2022