A ctive AED : A Human in the Loop Improves Annotation Error Detection (2023)

Abstract: Manually annotated datasets are crucial for training and evaluating Natural Language Processing models. However, recent work has discovered that even widely-used benchmark datasets contain a substantial number of erroneous annotations. This problem has been addressed with Annotation Error Detection (AED) models, which can flag such errors for human re-annotation. However, even though many of these AED methods assume a final curation step in which a human annotator decides whether the annotation is erroneous, they have been developed as static models without any human-in-the-loop component. In this work, we propose ActiveAED, an AED method that can detect errors more accurately by repeatedly querying a human for error corrections in its prediction loop. We evaluate ActiveAED on eight datasets spanning five different tasks and find that it leads to improvements over the state of the art on seven of them, with gains of up to six percentage points in average precision.

## introduction correct labels are crucial for model training and evaluation. wrongly labelled instances in the training data hamper model performance @xcite , whereas errors in the test data can lead to wrong estimates of model performance @xcite @xcite . this is a problem in practice, as even widely used benchmark datasets can contain a nonnegligible number of erroneous annotations @xcite @xcite . researchers have developed a multitude of annotation error detection (aed) methods to detect such labelling errors as recently surveyed by @xcite . after detection, there are multiple ways to deal with the found annotation errors. when it comes to training data, a reasonable strategy is to simply remove the instances flagged by an aed model @xcite . for evaluation data, however, this is not viable, because in many cases this would remove a significant fraction of hard but correctly labelled instances in addition to the errors @xcite , which would lead to an overestimation of model performance. instead, researchers resorted to manual correction of the labels flagged by the aed method @xcite @xcite . strikingly, even though this manual correction requires human input, the typical workflow is to first apply the aed method once and afterwards correct the flagged errors, without using the human feedback in the aed step. we hypothesize that connecting the human input and the aed prediction in a human-in-theloop setup could increase the accuracy of the aed method without increasing the total amount of human intervention. to support this hypothesis, we propose activeaed, an aed method which includes human feedback in the annotation loop; see figure 1 for an illustration. we base activeaed on the area-under-the-margin metric (aum) @xcite , which was recently proposed to detect annotation errors in computer vision datasets. as an additional contribution, we propose a novel ensembling scheme to improve aum's performance. in experiments on eight datasets spanning five different tasks, we show that activeaed improves over three baselines that performed well in a recent evaluation @xcite . on seven datasets, we observe improvements, with gains of up to six percentage points (pp) in average precision. our ablation study shows that both the human-in-the-loop component and the ensembling scheme contribute to the improvements. we make code and data available under https://github.com/mainlp/activeaed . ## related work aed for natural language processing (nlp) datasets has a long tradition which has recently been comprehensively evaluated and surveyed by the seminal work of @xcite . we base our evaluation setup on theirs. existing aed methods can be divided into six different categories @xcite @xcite , model-based @xcite @xcite , trainingdynamics-based @xcite @xcite , vector-spaceproximity-based @xcite , ensembling-based @xcite and rule-based @xcite . to the best of our knowledge, none of these aed methods has been developed or evaluated with a human in the loop, except for vlachos (2006) who uses aed as part a larger framework for constructing a silver-standard dataset. accordingly, they do not compare the performance of the aed component to competing approaches and they consider only a single dataset and task. additionally, one can distinguish between flaggers and scorers for aed @xcite . flaggers output hard decisions of whether an instance contains an error, whereas scorers assign to each instance a score reflecting the likelihood of being an error. in this work, we focus on scoring methods, because activeaed requires error scores to rank the instances. ## active annotation error detection we propose activeaed, an aed method which uses the error corrections issued by an annotator in its prediction loop. the basic procedure of ac-tiveaed is this: in the first step, it uses a ranking-based aed method to find the k most likely annotation errors across the dataset. in the second step, the presumed annotation errors are forwarded to an annotator who checks them and corrects the labels if necessary. after this, the dataset is updated with the corrections issued by the annotator and the procedure continues with the first step. this loop continues until a stopping condition is met, e.g. that the fraction of errors in the batch drops to a user-defined threshold. see figure 1 for an illustration of the process. we consider a scenario where an annotator wants to correct annotation errors in a dataset with a given annotation budget of n instances. there are two options of how to apply an annotation error detection (aed) method to support this. the first is the state-of-the-art and the second one is our proposed approach: (1) run the aed method once on the dataset to retrieve a list of instances ranked by their probability of containing an annotation error. then, spend the annotation budget by correcting the top-n instances. (2) run the aed method and spend some of the annotation budget by correcting the top-k instances with k « n. then, run the aed method again on the now partially corrected dataset and repeat until the annotation budget is exhausted. note, both approaches involve ranking instances based on their probability of containing annotation errors, and selection of a subset of instances for annotation based on this ranking. as a result, the outputs of both approaches can be fairly compared, because they use the same annotation budget and the same ranking-based score. more formally, we assume a dataset with inputs x, (potentially erroneous) labels y, and true labels y * which are initially unknown to us. after training the model for e epochs, we use (negative) aum to assign error scores: we extend aum with a novel ensembling scheme based on training dynamics. for this, we train a model for e epochs in a c-fold crossvalidation setup. for each fold @xmath0, ..., c} and epoch @xmath1, ..., e}, we obtain a model θ c,e . we use the models of one fold c to assign an error score s c,i to each instance with aum (eq. 1). for each fold, we calculate the aum score both on the train and on the test portion of the fold, which yields c -1 training-based scores and one test-based score for each instance. for each instance, we first average the training-based scores and then compute the mean of this average and the test-based score, which results in the final score s i : ## results the results of our evaluation can be found in it is instructive to compare the precision-recall curves of activeaed to that of its non-active variant. the graphs for datasets si flights and conll-2003 can be found in figure 2 . on both datasets, the precision gains are present in the mid-to-high recall regime (> 0.4), which intuitively makes sense, because activeaed requires a few rounds of human annotation to produce different outputs than its non-active variant. this suggests that one could increase the efficiency of activeaed by starting with a more lightweight aed method, e.g. one that does not require cross validation or ensembling and only later switch to the more compute-intensive ensembling of activeaed. we leave the investigation of this option for future work. we describe the ablation study of our proposed ensembling scheme and for different choices of k in appendix c. here, we find that test ensembling is crucial, that train ensembling sometimes improves results and that increasing k for the small si-flights dataset harms results. we provide example outputs of activeaed in appendix e. ## conclusion we have proposed activeaed, an aed method that includes human feedback in its prediction loop. while the proposed approach could be used with every ranking-based aed method, we base ac-tiveaed on the recently proposed aum score, which we augment with a novel ensembling scheme based on training dynamics. we evaluate ac-tiveaed on eight datasets spanning five different tasks and find that it improves results on seven of them, with gains of up to six pp ap. in future work, we plan on extending activeaed to generative models and structured prediction tasks. additionally, we want to use activeaed to clean benchmark datasets. we also plan to investigate the reasons for the observed performance gains of activeaed, for instance by exploring the role of model capacity and dataset characteristics @xcite . finally, we would like to study the interplay between activeaed and human label variation @xcite .