Towards Understanding Gender Bias in Relation Extraction (2020)

Abstract: 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.

## introduction with the wealth of information being posted online daily, relation extraction has become increasingly important. relation extraction aims specifically to extract relations from raw sentences and represent them as succinct relation tuples of the form (head, relation, tail) e.g., (barack obama, spouse, michelle obama). ## wikigenderbias we define gender bias in nre as a difference in model performance when predicting on sentences from male versus female articles. thus, we need articles written about entities for which we can identify the gender information. however, to obtain gender information for existing annotated datasets could be costly or impossible. thus, we elected to create wikigenderbias with this gender information to be able to detect scenarios like that in figure 1 . the data statistics of wikigenderbias are given in ## gender bias in nre we evaluate opennre @xcite , a popular open-source nre system. opennre implements the approach from @xcite . to convert sentences into vectors, researchers propose convolutional neural networks as well as the pieceweise convoultional neural networks (pcnn) which retain more structural information between entities @xcite . in this work, we use a pcnn with selective attention for the experiments. we train every encoder-selector combination on the training set of wikigenderbias and its genderequalized version. we input word2vec @xcite word embeddings trained on wikigen-derbias to the models foot_1 . we use commit 709b2f from the opennre repository tensorflow branch to obtain the models. ## bias mitigation we examine data augmentation and hard-debiasing as bias mitigation techniques for reducing gender bias in nre system. ## conclusion in our study, we create and publicly release wiki-genderbias: the first dataset aimed at evaluating bias in nre models. we train nre models on the wikigenderbias dataset and test them on genderseparated test sets. we find a difference in f1 scores for the spouse relation between predictions on male sentences and female for the model's predictions. we also examine existing bias mitigation techniques and find that naive data augmentation causes a significant performance drop. it is an open and difficult research question to build unbiased neural relation extractors. one possibility is that some bias mitigation methods that add noise to the dataset encourage neural relation extraction models to learn spurious correlations and unwanted biases. we encourage future work to dive deeper into this problem. while these findings will help future work avoid gender biases, this study is preliminary. we only consider binary gender, but future work should consider non-binary genders. additionally, future work should further probe the source of gender bias in the model's predictions, perhaps by visualizing attention or looking more closely at the model's outputs.