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Identifying Distributional Perspective Differences from Colingual Groups

Yufei Tian, Tuhin Chakrabarty, Fred Morstatter, and Nanyun Peng, in NAACL 2021 Workshop of Social NLP, 2021.

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Abstract

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.


Bib Entry

@inproceedings{tian2021identifying,
  title = {Identifying Distributional Perspective Differences from Colingual Groups},
  author = {Tian, Yufei and Chakrabarty, Tuhin and Morstatter, Fred and Peng, Nanyun},
  booktitle = {NAACL 2021 Workshop of Social NLP},
  year = {2021}
}

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    Full Text Code Abstract BibTeX Details
    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.
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      title = {Identifying Distributional Perspective Differences from Colingual Groups},
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      booktitle = {NAACL 2021 Workshop of Social NLP},
      year = {2021}
    }
    
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    @inproceedings{mullenbach2019nuclear,
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