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Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware methods are developed. However, most of existing definitions and methods focus only on single-label classification. Fairness for multi-label classification, where each instance is associated with more than one labels, is still yet to establish. To fill this gap, we study fairness-aware multi-label classification in this paper. We start by extending Demographic Parity (DP) and Equalized Opportunity (EOp), two popular fairness notions, to multi-label classification scenarios. Through a systematic study, we show that on multi-label data, because of unevenly distributed labels, EOp usually fails to construct a reliable estimate on labels with few instances. We then propose a new framework named Similarity s-induced Fairness (sγ -SimFair). This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp. Theoretical analysis and experimental results on real-world datasets together demonstrate the advantage of sγ -SimFair over existing methods on multi-label classification tasks.

 

In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives. The first perspective is to extract genuinely based on contextual description. To achieve this, we propose to conduct counterfactual analysis to attenuate the effects of two significant types of training biases: the event trigger bias and the frequent label bias. We also add tense information into event representations to explicitly place an emphasis on the contextual description. The second perspective is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text. By parameterization of Dirichlet Prior over the model-predicted categorical distribution, we improve the model estimates of the correctness likelihood and make TempRel predictions more selective. We also employ temperature scaling to recalibrate the model confidence measure after bias mitigation. Through experimental analysis on MATRES, MATRES-DS, and TDDiscourse, we demonstrate that our model extracts TempRel and timelines more faithfully compared to SOTA methods, especially under distribution shifts. 

Natural language often describes events in different granularities, such that more coarse-grained (goal) events can often be decomposed into fine-grained sequences of (step) events. A critical but overlooked challenge in understanding an event process lies in the fact that the step events are not equally important to the central goal. In this paper, we seek to fill this gap by studying how well current models can understand the essentiality of different step events towards a goal event. As discussed by cognitive studies, such an ability enables the machine to mimic human’s commonsense reasoning about preconditions and necessary efforts of daily-life tasks. Our work contributes with a high-quality corpus of (goal, step) pairs from a community guideline website WikiHow, where the steps are manually annotated with their essentiality w.r.t. the goal. The high IAA indicates that humans have a consistent understanding of the events. Despite evaluating various statistical and massive pre-trained NLU models, we observe that existing SOTA models all perform drastically behind humans, indicating the need for future investigation of this crucial yet challenging task.