Were the Wikipedia edits that removed framings, presuppositions and attitudes from biased sentences a Wiki community effort, or were annotators trained to do it?
Wiki community effort
Texts like news, encyclopedias, and some social media strive for objectivity. Yet bias in the form of inappropriate subjectivity - introducing attitudes via framing, presupposing truth, and casting doubt - remains ubiquitous. This kind of bias erodes our collective trust and fuels social conflict. To address this issue, we introduce a novel testbed for natural language generation: automatically bringing inappropriately subjective text into a neutral point of view ("neutralizing" biased text). We also offer the first parallel corpus of biased language. The corpus contains 180,000 sentence pairs and originates from Wikipedia edits that removed various framings, presuppositions, and attitudes from biased sentences. Last, we propose two strong encoder-decoder baselines for the task. A straightforward yet opaque CONCURRENT system uses a BERT encoder to identify subjective words as part of the generation process. An interpretable and controllable MODULAR algorithm separates these steps, using (1) a BERT-based classifier to identify problematic words and (2) a novel join embedding through which the classifier can edit the hidden states of the encoder. Large-scale human evaluation across four domains (encyclopedias, news headlines, books, and political speeches) suggests that these algorithms are a first step towards the automatic identification and reduction of bias.
Writers and editors of texts like encyclopedias, news, and textbooks strive to avoid biased language. Yet bias remains ubiquitous. 62% of Americans believe their news is biased BIBREF0 and bias is the single largest source of distrust in the media BIBREF1. This work presents data and algorithms for automatically reducing bias in text. We focus on a particular kind of bias: inappropriate subjectivity (“subjective bias”). Subjective bias occurs when language that should be neutral and fair is skewed by feeling, opinion, or taste (whether consciously or unconsciously). In practice, we identify subjective bias via the method of BIBREF2: using Wikipedia's neutral point of view (NPOV) policy. This policy is a set of principles which includes “avoiding stating opinions as facts” and “preferring nonjudgemental language”. For example a news headline like “John McCain exposed as an unprincipled politician" (Figure FIGREF1) is biased because the verb expose is a factive verb that presupposes the truth of its complement; a non-biased sentence would use a verb like describe so as not to presuppose something that is the subjective opinion of the writer. “Pilfered” in “the gameplay is pilfered from DDR” (Table TABREF3) subjectively frames the shared gameplay as a kind of theft. “His” in “a lead programmer usually spends his career” again introduces a biased and subjective viewpoint (that all programmers are men) through presupposition. We aim to debias text by suggesting edits that would make it more neutral. This contrasts with prior research which has debiased representations of text by removing dimensions of prejudice from word embeddings BIBREF3, BIBREF4 and the hidden states of predictive models BIBREF5, BIBREF6. To avoid overloading the definition of “debias,” we refer to our kind of text debiasing as neutralizing that text. Figure FIGREF1 gives an example. We introduce the Wiki Neutrality Corpus (WNC). This is a new parallel corpus of 180,000 biased and neutralized sentence pairs along with contextual sentences and metadata. The corpus was harvested from Wikipedia edits that were designed to ensure texts had a neutral point of view. WNC is the first parallel corpus targeting biased and neutralized language. We also define the task of neutralizing subjectively biased text. This task shares many properties with tasks like detecting framing or epistemological bias BIBREF2, or veridicality assessment/factuality prediction BIBREF7, BIBREF8, BIBREF9, BIBREF10. Our new task extends these detection/classification problems into a generation task: generating more neutral text with otherwise similar meaning. Finally, we propose a pair of novel sequence-to-sequence algorithms for this neutralization task. Both methods leverage denoising autoencoders and a token-weighted loss function. An interpretable and controllable modular algorithm breaks the problem into (1) detection and (2) editing, using (1) a BERT-based detector to explicitly identify problematic words, and (2) a novel join embedding through which the detector can modify an editors' hidden states. This paradigm advances an important human-in-the-loop approach to bias understanding and generative language modeling. Second, an easy to train and use but more opaque concurrent system uses a BERT encoder to identify subjectivity as part of the generation process. Large-scale human evaluation suggests that while not without flaws, our algorithms can identify and reduce bias in encyclopedias, news, books, and political speeches, and do so better than state-of-the-art style transfer and machine translation systems. This work represents an important first step towards automatically managing bias in the real world. We release data and code to the public.