Swat-Mp

Katz, Phil; Singleton, Matt; Wicentowski, Richard

doi:10.3115/1621474.1621541

Cited by 60 publications

(1 citation statement)

References 3 publications

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“…We explore tasks for social applications, specifically perceived emotion detection and perceived value detection. Emotion prediction has been studied for many different domains (Strapparava and Mihalcea, 2007;Katz et al, 2007;Ezhilarasi and Minu, 2012;Chen et al, 2018;Mohammadi et al, 2019), and has been extensively applied to social media posts (Mohammad, 2012;Wang et al, 2012;Mohammad and Kiritchenko, 2015;Abdul-Mageed and Ungar, 2017), particularly in the social good domain. Sharifirad et al (2019)…”

Section: Social Applicationsmentioning

confidence: 99%

Social Commonsense for Explanation and Cultural Bias Discovery

Bauer,

Tischer,

Bansal

2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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Social commonsense contains many human biases due to social and cultural influence (Sap et al., 2020;Emelin et al., 2020). We focus on identifying cultural biases in data, specifically causal assumptions and commonsense implications, that strongly influence model decisions for a variety of tasks designed for social impact. This enables us to examine data for bias by making explicit the causal (if-then, inferential) relations in social commonsense knowledge used for decision making, furthering interpretable commonsense reasoning from a dataset perspective. We apply our methods on 2 social tasks: emotion detection and perceived value detection. We identify influential social commonsense knowledge to explain model behavior in the following ways. First, we augment large-scale language models with social knowledge and show improvements for the tasks, indicating the implicit assumptions a model requires to be successful on each dataset. Second, we identify influential events in the datasets by using social knowledge to cluster data and demonstrate the influence that these events have on model behavior via leave-K-out experiments. This allows us to gain a datasetlevel understanding of the events and causal commonsense relationships that strongly influence predictions. We then analyze these relationships to detect influential cultural bias in each dataset. Finally, we use our influential event identification for detecting mislabeled examples and improve training and performance through their removal. We support our findings with manual analysis.'PersonX sells ___ to the public' ATOMIC knowledge:'generous', 'helpful', 'influential', 'professional' Social Signif.

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