62 citations · 95 across the 9 of their papers we have counts for
8 papers · 1 filter
Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children's Fairy Tales
Paulina Toro Isaza, Guangxuan Xu, Akintoye Oloko +3
Social biases and stereotypes are embedded in our culture in part through their presence in our stories, as evidenced by the rich history of humanities and social science literatur…
EnDex: Evaluation of Dialogue Engagingness at Scale
Guangxuan Xu, Ruibo Liu, Fabrice Harel-Canada +2
We propose EnDex, the first human-reaction based model to evaluate dialogue engagingness. EnDex is trained on 80k Reddit-based Engagement Dataset (RED) curated using a novel distan…
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…
Can Model Compression Improve NLP Fairness
Guangxuan Xu, Qingyuan Hu
Model compression techniques are receiving increasing attention; however, the effect of compression on model fairness is still under explored. This is the first paper to examine th…
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark
Hao Sun, Guangxuan Xu, Jiawen Deng +6
Dialogue safety problems severely limit the real-world deployment of neural conversational models and have attracted great research interests recently. However, dialogue safety pro…
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…
Enhanced Offensive Language Detection Through Data Augmentation
Ruibo Liu, Guangxuan Xu, Soroush Vosoughi
Detecting offensive language on social media is an important task. The ICWSM-2020 Data Challenge Task 2 is aimed at identifying offensive content using a crowd-sourced dataset cont…
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…