Reporting Bias and Knowledge Extraction
By Jonathan Gordon, Benjamin Van Durme. 2013
20 citations recorded by the source corpus.
Subfields: Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Yejin Choi
- Mohit Bansal
- Kai-Wei Chang
- Jianfeng Gao
- Jason Aaron Edward Weston
- Devi Parikh
- Benjamin Van Durme
- Rachel Rudinger
Citations in the corpus
- Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (2016, background): “However, such counts are often misleading [14].”
- VQA: Visual Question Answering (2015, background): “nformation about visual scenes; although both captions and questions & answers provide information about the visual world, they do it from different perspectives, with different underlying biases [20], and can function as complementary to one another. 6. Noun tags begin with NN, verb tags begin ”
- Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints (2017, background): “Data collection biases have been discussed in the context of creating image corpus (Misra et al., 2016; van Miltenburg, 2016) and text corpus (Gordon and Van Durme, 2013; Van Durme, 2010).”
- Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels (2015, background): “Instead, it leads to a classifier that attempts to mimic the reporting bias of the annotators.”
- Ordinal Common-sense Inference (2016, background): “5 4 For further background see discussions by Van Durme (2010), Gordon and Van Durme (2013), Rudinger et al. (2015) and Misra et al. (2016).”
- Deconfounded Image Captioning: A Causal Retrospect (2020, background): “However, since humans are living in a world filled with various biases, e.g., long-tailed concept distributions [4], [5], reporting bias [6], [7], and language bias [8], [9], we are easily trapped in the loop: “making a dataset”–“it’s biased”–“making a new one”.”
- A Survey of Current Datasets for Vision and Language Research (2015, background): “Datasets are affected by both reporting bias (Gordon and Durme, 2013), where the frequency with which people write about actions, events, or states does not directly reflect real-world frequencies of those phenomena; they are also affected by photographer’s bias (Torralba and Efros, 2011), where…”