Computer Science > Computation and Language
arXiv:2303.05670 (cs)
[Submitted on 10 Mar 2023]
Title:Logic Against Bias: Textual Entailment Mitigates Stereotypical Sentence Reasoning
View a PDF of the paper titled Logic Against Bias: Textual Entailment Mitigates Stereotypical Sentence Reasoning, by Hongyin Luo and 1 other authors
View PDFAbstract:Due to their similarity-based learning objectives, pretrained sentence encoders often internalize stereotypical assumptions that reflect the social biases that exist within their training corpora. In this paper, we describe several kinds of stereotypes concerning different communities that are present in popular sentence representation models, including pretrained next sentence prediction and contrastive sentence representation models. We compare such models to textual entailment models that learn language logic for a variety of downstream language understanding tasks. By comparing strong pretrained models based on text similarity with textual entailment learning, we conclude that the explicit logic learning with textual entailment can significantly reduce bias and improve the recognition of social communities, without an explicit de-biasing process
| Comments: | Accepted by EACL 2023 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2303.05670 [cs.CL] |
| (orarXiv:2303.05670v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2303.05670 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Logic Against Bias: Textual Entailment Mitigates Stereotypical Sentence Reasoning, by Hongyin Luo and 1 other authors
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