In this presentation, I am going to present our Findings Paper on WIKIBIAS. Biases continue to be prevalent in modern text
and media, especially subjective bias – a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth. To tackle the problem of detecting and further mitigating subjective bias,
we introduce a manually annotated parallel
corpus WIKIBIAS with more than 4,000 sentence pairs from Wikipedia edits. This corpus
contains annotations towards both sentence-level bias types and token-level biased segments. We present systematic analyses of our
dataset and results achieved by a set of state-of-the-art baselines in terms of three tasks: bias
classification, tagging biased segments, and
neutralizing biased text. We find that current
models still struggle with detecting multi-span
biases despite their reasonable performances,
suggesting that our dataset can serve as a useful research benchmark. We also demonstrate
that models trained on our dataset can generalize well to multiple domains such as news and
political speeches.

WIKIBIAS: Detecting Multi-Span Subjective Biases in Language

Biases continue to be prevalent in modern text
and media, especially subjective bias – a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth. To tackle the problem of detecting and further mitigating subjective bias,
we introduce a manually annotated parallel
corpus WIKIBIAS with more than 4,000 sentence pairs from Wikipedia edits. This corpus
contains annotations towards both sentence-level bias types and token-level biased segments. We present systematic analyses of our
dataset and results achieved by a set of state-of-the-art baselines in terms of three tasks: bias
classification, tagging biased segments, and
neutralizing biased text. We find that current
models still struggle with detecting multi-span
biases despite their reasonable performances,
suggesting that our dataset can serve as a useful research benchmark. We also demonstrate
that models trained on our dataset can generalize well to multiple domains such as news
and political speeches.



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W7: 7th Workshop on Noisy User-generated Text (W-NUT 2021) Part I

workshop paper

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Yang Zhong

2

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WIKIBIAS: Detecting Multi-Span Subjective Biases in Language

WIKIBIAS: Detecting Multi-Span Subjective Biases in Language

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