11 research outputs found
Disembodied Machine Learning: On the Illusion of Objectivity in NLP
Machine Learning seeks to identify and encode bodies of knowledge within
provided datasets. However, data encodes subjective content, which determines
the possible outcomes of the models trained on it. Because such subjectivity
enables marginalisation of parts of society, it is termed (social) `bias' and
sought to be removed. In this paper, we contextualise this discourse of bias in
the ML community against the subjective choices in the development process.
Through a consideration of how choices in data and model development construct
subjectivity, or biases that are represented in a model, we argue that
addressing and mitigating biases is near-impossible. This is because both data
and ML models are objects for which meaning is made in each step of the
development pipeline, from data selection over annotation to model training and
analysis. Accordingly, we find the prevalent discourse of bias limiting in its
ability to address social marginalisation. We recommend to be conscientious of
this, and to accept that de-biasing methods only correct for a fraction of
biases.Comment: In revie
Dynabench: Rethinking Benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop dataset creation: annotators seek to create examples that a target model will misclassify, but that another person will not. In this paper, we argue that Dynabench addresses a critical need in our community: contemporary models quickly achieve outstanding performance on benchmark tasks but nonetheless fail on simple challenge examples and falter in real-world scenarios. With Dynabench, dataset creation, model development, and model assessment can directly inform each other, leading to more robust and informative benchmarks. We report on four initial NLP tasks, illustrating these concepts and highlighting the promise of the platform, and address potential objections to dynamic benchmarking as a new standard for the field
Findings from the Hackathon on Understanding Euroscepticism Through the Lens of Textual Data
We present an overview and the results of a shared-task hackathon that took place as part of a research seminar bringing together a variety of experts and young researchers from the fields of political science, natural language processing and computational social science. The task looked at ways to develop novel methods for political text scaling to better quantify political party positions on European integration and Euroscepticism from the transcript of speeches of three legislations of the European Parliament
Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter
Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter
Hate speech in the form of racist and sexist remarks are a common occurrence on social media. For that reason, many social media services address the problem of identifying hate speech, but the definition of hate speech varies markedly and is largely a manual effort. We provide a list of criteria founded in critical race theory, and use them to annotate a publicly available corpus of more than 16k tweets. We analyze the impact of various extra-linguistic features in conjunction with character n-grams for hate-speech detection. We also present a dictionary based the most indicative words in our data
HateCheck: Functional Tests for Hate Speech Detection Models
Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck's utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses