62 citations · 89 across the 4 of their papers we have counts for
5 papers
Non-Parallel Text Style Transfer with Self-Parallel Supervision
Ruibo Liu, Chongyang Gao, Chenyan Jia +2
The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping…
Modulating Language Models with Emotions
Ruibo Liu, Jason Wei, Chenyan Jia +1
Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems. In this paper, we propose a formulation of modulated…
Mitigating Political Bias in Language Models Through Reinforced Calibration
Ruibo Liu, Chenyan Jia, Jason Wei +3
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world…
Political Depolarization of News Articles Using Attribute-aware Word Embeddings
Ruibo Liu, Lili Wang, Chenyan Jia +1
Political polarization in the US is on the rise. This polarization negatively affects the public sphere by contributing to the creation of ideological echo chambers. In this paper,…
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
Ruibo Liu, Guangxuan Xu, Chenyan Jia +3
Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augme…