Adapting Coreference Resolution Models through Active Learning (2022)

Abstract: Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.

## introduction linguistic expressions are coreferent if they refer to the same entity. the computational task of discovering coreferent mentions is coreference resolution (cr). neural models @xcite ) are sota on ontonotes 5.0 @xcite but cannot immediately generalize to other datasets. generalization is difficult because domains differ in content, writing style, and annotation guidelines. to overcome these challenges, models need copiously labeled, in-domain data @xcite . despite expensive labeling costs, adapting cr is crucial for applications like uncovering information about proteins in biomedicine @xcite and distinguishing entities in legal documents @xcite . ideally, we would like to quickly and cheaply adapt the model without repeatedly relying on an excessive amount of annotations to retrain the model. to reduce labeling cost, we investigate active learning @xcite for cr. active learning aims to reduce annotation costs by intelligently selecting examples to label. prior approaches use active learning to improve the model within the same domain @xcite without considering adapting to new data distributions. for domain adaptation in cr, @xcite motivate the use of active learning to select out-of-distribution examples. a word like "the bonds" refers to municipal bonds in ontonotes but links to "chemical bonds" in another domain (figure 1 ). if users annotate the antecedents of "the bonds" and other ambiguous entity mentions, then these labels help adapt a model trained on ontonotes to new domains. active learning for cr adaptation is wellmotivated, but the implementation is neither straightforward nor well-studied. first, cr is a span detection and clustering task, so selecting which spans to label is more complicated than choosing independent examples for text classification. second, cr labeling involves closely reading the documents. labeling more spans within the same context is more efficient. however, labeling more spans across different documents increases data diversity and may improve model transfer. how should we balance these competing objectives? our paper extends prior work in active learning for cr to the problem of coreference model transfer @xcite : figure 1 : cr models are trained on source domain ontonotes, which contains data like news articles. the source document links "the bonds" to "municipal bonds". in a target domain like preco @xcite , "the bonds" may no longer have the same meaning. it can refer to "chemical bonds" (document 1) or not be considered an entity (document 2). a solution is to continue training the source model on more spans from the target domain. active learning helps select ambiguous spans, like "the bonds", for the user to label on this interface (section 4.2). the scores s(x, y) are computed by the model's pairwise scorer (appendix a.1). cr models like c2f-coref are typically trained on ontonotes. recent work in cr improves upon c2f-coref and has sota results on ontonotes @xcite . however, annotation guidelines and the underlying text differ across domains. as a result, these cr models cannot immediately transfer to other datasets. for different domains, spans could hold different meanings or link to different entities. @xcite show the benefits of continued training where a model trained on ontonotes is further trained on the target dataset. for several target domains, continued training from ontonotes is stronger than training the model from scratch, especially when the training dataset is small. their experiments use an incremental variant of c2f-coref called icoref @xcite . while c2f-coref requires θ(n) memory to simultaneously access all spans in the document and infer a span's antecedent, icoref only needs constant memory to predict a span's entity cluster. despite using less space, icoref retains the same accuracy as c2f-coref. rather than assigning x to antecedent y, icoref assigns x to cluster c where c is from a set of observed entity clusters c, as the algorithm processes spans in the document, each span is either placed in a cluster from c or added to a new cluster. to learn the distribution over clusters (equation 2 ), the algorithm first creates a cluster representation that is an aggregate of span representations over spans that currently exist in the cluster. with cluster and span representations, individual spans and entity clusters are mapped into a shared space. then, we can compute s(x, c) using the same pairwise scorer as before. @xcite show that continued training is useful for domain adaptation but assume that labeled data already exist in the target domain. however, model transfer is more critical when annotations are scarce. thus, the question becomes: how can we adapt cr models without requiring a large, labeled dataset? our paper investigates active learning as a potential solution. through active learning, we reduce labeling costs by sampling and annotating a small subset of ambiguous spans. ## method: active learning neural models achieve high accuracy for ontonotes but cannot quickly adapt to new datasets because of shifts in domain or annotation standards @xcite . to transfer to new domains, models need substantial in-domain, labeled data. in low-resource situations, cr is infeasible for real-time applications. to reduce the labeling burden, active learning may target spans that most confuse the model. active learning for domain adaptation @xcite typically proceeds as follows: begin with a model trained on source data, sample and label k spans from documents in the target domain based on a strategy, and train the model on labeled data. this labeling setup may appear straightforward to apply to cr, but there are some tricky details. the first complication is that-unlike text classification-cr is a clustering task. early approaches in active learning for cr use pairwise annotations @xcite . pairs of spans are sampled and the annotator labels whether each pair is coreferent. the downside to pairwise annotations is that it requires many labels. to label the antecedent of entity mention x, x must be compared to every candidate span in the document. @xcite propose a new scheme called discrete annotations. instead of sampling pairs of spans, the active learning strategy samples individual spans. then, the annotator only has to find and label first antecedent of x in the document, which bypasses the multiple pairwise comparisons. thus, we use discrete annotations to minimize labeling. to further improve active learning for cr, we consider the following issues. first, the cr model has different scores for mention detection and linking, but prior active learning methods only considers linking. second, labeling cr requires time to read the document context. therefore, we explore important aspects of active learning for adapting cr: model uncertainty (section 3.1), and the balance between reading and labeling (section 3.2). ## active learning for cr through simulations and humans we run experiments to understand two important factors of active learning for cr: sources of model uncertainty (section 3.1) and balancing reading against labeling (sections 3.2). first, we simulate active learning on preco to compare sampling strategies based on various forms of uncertainty (section 4.1). then, we set up a user study to investigate how humans perform when labeling spans from fewer or more documents from preco (section 4.2). specifically, we analyze their annotation time and throughput. finally, we run large-scale simulations on preco and qbcoref (section 4.3). we explore different combinations of sampling strategies and labeling configurations. ## conclusion neural cr models desparately depend on large, labeled data. we use active learning to transfer a model trained on ontonotes, the "de facto" dataset, to new domains. active learning for cr is difficult because the problem does not only concern sampling examples. we must consider different aspects, like sources of model uncertainty and cost of reading documents. our work explores these factors through exhaustive simulations. additionally, we develop a user interface to run a user study from which we observe human annotation time and throughput. in both simulations and the user study, cr improves from continued training on spans sampled from the same document rather than different contexts. surprisingly, sampling by entropy in mention detection, rather than linking, is most helpful for domains like preco. this opposes the assumption that the uncertainty strategy must be directly tied to the training objective. future work may extend our contributions to multilingual transfer or multi-component tasks, like open-domain qa.