Jinrui Yang


2023

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Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval
Jinrui Yang | Timothy Baldwin | Trevor Cohn
Proceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL)

2022

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Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in English CVs
Jinrui Yang | Sheilla Njoto | Marc Cheong | Leah Ruppanner | Lea Frermann
Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)

Gender discrimination in hiring is a pertinent and persistent bias in society, and a common motivating example for exploring bias in NLP. However, the manifestation of gendered language in application materials has received limited attention. This paper investigates the framing of skills and background in CVs of self-identified men and women. We introduce a data set of 1.8K authentic, English-language, CVs from the US, covering 16 occupations, allowing us to partially control for the confound occupation-specific gender base rates. We find that (1) women use more verbs evoking impressions of low power; and (2) classifiers capture gender signal even after data balancing and removal of pronouns and named entities, and this holds for both transformer-based and linear classifiers.

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Towards Open-Domain Topic Classification
Hantian Ding | Jinrui Yang | Yuqian Deng | Hongming Zhang | Dan Roth
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

We introduce an open-domain topic classification system that accepts user-defined taxonomy in real time. Users will be able to classify a text snippet with respect to any candidate labels they want, and get instant response from our web interface. To obtain such flexibility, we build the backend model in a zero-shot way. By training on a new dataset constructed from Wikipedia, our label-aware text classifier can effectively utilize implicit knowledge in the pretrained language model to handle labels it has never seen before. We evaluate our model across four datasets from various domains with different label sets. Experiments show that the model significantly improves over existing zero-shot baselines in open-domain scenarios, and performs competitively with weakly-supervised models trained on in-domain data.