Towards Debiasing Frame Length Bias in Text-Video Retrieval via Causal Intervention

Many studies focus on improving pretraining or developing new backbones in text-video retrieval. However, existing methods may suffer from the learning and inference bias issue, as recent research suggests in other text-video-related tasks. For instance, spatial appearance features on action recognition or temporal object co-occurrences on video scene graph generation could induce spurious correlations. In this work, we present a unique and systematic study of a temporal bias due to frame length discrepancy between training and test sets of trimmed video clips, which is the first such attempt for a text-video retrieval task, to the best of our knowledge. We first hypothesise and verify the bias on how it would affect the model illustrated with a baseline study. Then, we propose a causal debiasing approach and perform extensive experiments and ablation studies on the Epic-Kitchens-100, YouCook2, and MSR-VTT datasets. Our model overpasses the baseline and SOTA on nDCG, a semantic-relevancy-focused evaluation metric which proves the bias is mitigated, as well as on the other conventional metrics.Comment: Accepted by the British Machine Vision Conference (BMVC) 2023. Project Page: https://buraksatar.github.io/FrameLengthBia

Survey on Sociodemographic Bias in Natural Language Processing

Deep neural networks often learn unintended biases during training, which might have harmful effects when deployed in real-world settings. This paper surveys 209 papers on bias in NLP models, most of which address sociodemographic bias. To better understand the distinction between bias and real-world harm, we turn to ideas from psychology and behavioral economics to propose a definition for sociodemographic bias. We identify three main categories of NLP bias research: types of bias, quantifying bias, and debiasing. We conclude that current approaches on quantifying bias face reliability issues, that many of the bias metrics do not relate to real-world biases, and that current debiasing techniques are superficial and hide bias rather than removing it. Finally, we provide recommendations for future work.Comment: 23 pages, 1 figur

Dissecting Deep Language Models: The Explainability and Bias Perspective

L'abstract è presente nell'allegato / the abstract is in the attachmen

Automatically Neutralizing Subjective Bias in Text

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.Comment: To appear at AAAI 202