Biased language commonly occurs around topics which are of controversial nature, thus, stirring disagreement between the different involved parties of a discussion. This is due to the fact that for language and its use, specifically, the understanding and use of phrases, the stances are cohesive within the particular groups. However, such cohesiveness does not hold across groups. In collaborative environments or environments where impartial language is desired (e.g. Wikipedia, news media), statements and the language therein should represent equally the involved parties and be neutrally phrased. Biased language is introduced through the presence of inflammatory words or phrases, or statements that may be incorrect or one-sided, thus violating such consensus. In this work, we focus on the specific case of phrasing bias, which may be introduced through specific inflammatory words or phrases in a statement. For this purpose, we propose an approach that relies on a recurrent neural networks in order to capture the inter-dependencies between words in a phrase that introduced bias. We perform a thorough experimental evaluation, where we show the advantages of a neural based approach over competitors that rely on word lexicons and other hand-crafted features in detecting biased language. We are able to distinguish biased statements with a precision of P=0.917, thus significantly outperforming baseline models with an improvement of over 30 . Finally, we release the largest corpus of statements annotated for biased language.
@cite_4 propose an approach for detecting a single bias-inducing word given a biased Wikipedia statement. The approach relies on linguistic features, divided into two bias classes: framing bias, including subjective language such as praising and perspective-specific words; and epistemological bias, dealing with believability of a proposition, i.e. phrasing choices that either cast doubt on a fact or try to sell an opinion as a fact. In their dataset collection, they crawl Wikipedia revisions that have a '' flag in the revision comments. We use a similar dataset collection procedure, however, we additionally use crowdsourcing to filter statements that do not contain bias ( @math 60
@cite_4
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Unbiased language is a requirement for reference sources like encyclopedias and scientific texts. Bias is, nonetheless, ubiquitous, making it crucial to understand its nature and linguistic realization and hence detect bias automatically. To this end we analyze real instances of human edits designed to remove bias from Wikipedia articles. The analysis uncovers two classes of bias: framing bias, such as praising or perspective-specific words, which we link to the literature on subjectivity; and epistemological bias, related to whether propositions that are presupposed or entailed in the text are uncontroversially accepted as true. We identify common linguistic cues for these classes, including factive verbs, implicatives, hedges, and subjective intensifiers. These insights help us develop features for a model to solve a new prediction task of practical importance: given a biased sentence, identify the bias-inducing word. Our linguistically-informed model performs almost as well as humans tested on the same task. Quality in Wikipedia is enforced through a set of editing policies and guidelines recommended for Wikipedia editors. Neutral point of view (NPOV) is one of the main principles in Wikipedia, which ensures that for controversial information all possible points of view are represented proportionally. Furthermore, language used in Wikipedia should be neutral and not opinionated. However, due to the large number of Wikipedia articles and its operating principle based on a voluntary basis of Wikipedia editors; quality assurances and Wikipedia guidelines cannot always be enforced. Currently, there are more than 40,000 articles, which are flagged with NPOV or similar quality tags. Furthermore, these represent only the portion of articles for which such quality issues are explicitly flagged by the Wikipedia editors, however, the real number may be higher considering that only a small percentage of articles are of good quality or featured as categorized by Wikipedia. In this work, we focus on the case of language bias at the sentence level in Wikipedia. Language bias is a hard problem, as it represents a subjective task and usually the linguistic cues are subtle and can be determined only through its context. We propose a supervised classification approach, which relies on an automatically created lexicon of bias words, and other syntactical and semantic characteristics of biased statements. We experimentally evaluate our approach on a dataset consisting of biased and unbiased statements, and show that we are able to detect biased statements with an accuracy of 74 . Furthermore, we show that competitors that determine bias words are not suitable for detecting biased statements, which we outperform with a relative improvement of over 20 . We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing coreference benchmark datasets. Our dataset and code are available at this http URL Given a partial description like "she opened the hood of the car," humans can reason about the situation and anticipate what might come next ("then, she examined the engine"). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We present SWAG, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-the-art language models to massively oversample a diverse set of potential counterfactuals. Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88 ), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research.