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- W3109469340 abstract "We investigate the impact of political ideology biases in training data. Through a set of comparison studies, we examine the propagation of biases in several widely-used NLP models and its effect on the overall retrieval accuracy. Our work highlights the susceptibility of large, complex models to propagating the biases from human-selected input, which may lead to a deterioration of retrieval accuracy, and the importance of controlling for these biases. Finally, as a way to mitigate the bias, we propose to learn a text representation that is invariant to political ideology while still judging topic relevance." @default.
- W3109469340 created "2020-12-07" @default.
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- W3109469340 date "2020-11-29" @default.
- W3109469340 modified "2023-10-16" @default.
- W3109469340 title "Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models" @default.
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- W3109469340 doi "https://doi.org/10.48550/arxiv.2011.14293" @default.
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