Diba Mirza


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Towards Understanding Gender Bias in Relation Extraction
Andrew Gaut | Tony Sun | Shirlyn Tang | Yuxin Huang | Jing Qian | Mai ElSherief | Jieyu Zhao | Diba Mirza | Elizabeth Belding | Kai-Wei Chang | William Yang Wang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction. While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to evaluate social biases exhibited in NRE systems. In this paper, we create WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10% human annotated test set for the purpose of analyzing gender bias in relation extraction systems. We find that when extracting spouse-of and hypernym (i.e., occupation) relations, an NRE system performs differently when the gender of the target entity is different. However, such disparity does not appear when extracting relations such as birthDate or birthPlace. We also analyze how existing bias mitigation techniques, such as name anonymization, word embedding debiasing, and data augmentation affect the NRE system in terms of maintaining the test performance and reducing biases. Unfortunately, due to NRE models rely heavily on surface level cues, we find that existing bias mitigation approaches have a negative effect on NRE. Our analysis lays groundwork for future quantifying and mitigating bias in NRE.

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Investigating African-American Vernacular English in Transformer-Based Text Generation
Sophie Groenwold | Lily Ou | Aesha Parekh | Samhita Honnavalli | Sharon Levy | Diba Mirza | William Yang Wang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The growth of social media has encouraged the written use of African American Vernacular English (AAVE), which has traditionally been used only in oral contexts. However, NLP models have historically been developed using dominant English varieties, such as Standard American English (SAE), due to text corpora availability. We investigate the performance of GPT-2 on AAVE text by creating a dataset of intent-equivalent parallel AAVE/SAE tweet pairs, thereby isolating syntactic structure and AAVE- or SAE-specific language for each pair. We evaluate each sample and its GPT-2 generated text with pretrained sentiment classifiers and find that while AAVE text results in more classifications of negative sentiment than SAE, the use of GPT-2 generally increases occurrences of positive sentiment for both. Additionally, we conduct human evaluation of AAVE and SAE text generated with GPT-2 to compare contextual rigor and overall quality.


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Mitigating Gender Bias in Natural Language Processing: Literature Review
Tony Sun | Andrew Gaut | Shirlyn Tang | Yuxin Huang | Mai ElSherief | Jieyu Zhao | Diba Mirza | Elizabeth Belding | Kai-Wei Chang | William Yang Wang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.