Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conceptor Debiasing of Word Representations Evaluated on WEAT   Bias in word embeddings such as Word2Vec has been widely investigated, and
many efforts made to remove such bias. We show how to use conceptors debiasing
to post-process both traditional and contextualized word embeddings. Our
conceptor debiasing can simultaneously remove racial and gender biases and,
unlike standard debiasing methods, can make effect use of heterogeneous lists
of biased words. We show that conceptor debiasing diminishes racial and gender
bias of word representations as measured using the Word Embedding Association
Test (WEAT) of Caliskan et al. (2017).

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The categories of this paper are: eess
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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: physics
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: math
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The categories of this paper are: comp-gas