Papers by Dylan Walker

    1 papers
    Ideology Prediction from Scarce and Biased Supervision: Learn to Disregard the “What” and Focus on the “How”! (2023.acl-long)

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    Challenge: a novel supervised learning approach for political ideology prediction is needed for many applications.
    Approach: They propose a supervised learning approach for political ideology prediction that decomposes document embeddings into a linear superposition of two vectors.
    Outcome: The proposed model outperforms state-of-the-art models on two benchmark datasets with biased data with 5% accuracy.

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