Identifying Distributional Perspective Differences from Colingual Groups
This work addresses the need for culturally-aware models in NLP to reduce biases and improve mutual understanding across groups, though it is incremental in applying existing methods to new data.
The paper tackled the problem of automatically identifying distributional perspective differences among colingual groups using language corpora, achieving high correlation with human judgments on topics like marriage and democracy.
Perspective differences exist among different cultures or languages. A lack of mutual understanding among different groups about their perspectives on specific values or events may lead to uninformed decisions or biased opinions. Automatically understanding the group perspectives can provide essential background for many downstream applications of natural language processing techniques. In this paper, we study colingual groups and use language corpora as a proxy to identify their distributional perspectives. We present a novel computational approach to learn shared understandings, and benchmark our method by building culturally-aware models for the English, Chinese, and Japanese languages. On a held out set of diverse topics including marriage, corruption, democracy, our model achieves high correlation with human judgements regarding intra-group values and inter-group differences.