A Multi-task Learning Model for Fine-Grain Dialogue Social Bias Measurement

Hanjie Mai, Xiaobing Zhou, Liqing Wang

Abstract: In recent years, the use of NLP models to predict people’s attitudes toward social bias has attracted the attention of many researchers. In the existing work, most of the research is at the sentence level, i.e., judging whether the whole sentence has a biased property. In this work, we leverage pre-trained models’ powerful semantic modeling capabilities to model dialogue context. Furthermore, to use more features to improve the ability of the model to identify bias, we propose two auxiliary tasks with the help of the dialogue’s topic and type features. In order to achieve better classification results, we use the adversarial training method to train two multi-task models. Finally, we combine the two multi-task models by voting. We participated in the NLPCC-2022 shared task on Fine-Grain Dialogue Social Bias Measurement and ranked fourth with the Macro-F1 score of 0.5765. The codes of our model are available at github ( https://github.com/33Da/nlpcc2022-task7 ).