Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing (2022)

Abstract: Entity typing aims at predicting one or more words that describe the type(s) of a specific mention in a sentence. Due to shortcuts from surface patterns to annotated entity labels and biased training, existing entity typing models are subject to the problem of spurious correlations. To comprehensively investigate the faithfulness and reliability of entity typing methods, we first systematically define distinct kinds of model biases that are reflected mainly from spurious correlations. Particularly, we identify six types of existing model biases, including mention-context bias, lexical overlapping bias, named entity bias, pronoun bias, dependency bias, and overgeneralization bias. To mitigate model biases, we then introduce a counterfactual data augmentation method. By augmenting the original training set with their debiasedcounterparts, models are forced to fully comprehend sentences and discover the fundamental cues for entity typing, rather than relying on spurious correlations for shortcuts. Experimental results on the UFET dataset show our counterfactual data augmentation approach helps improve generalization of different entity typing models with consistently better performance on both the original and debiased test sets.

## introduction given a sentence with an entity mention, the entity typing task aims at predicting one or more words or phrases that describe the type(s) of that specific mention @xcite @xcite . this task essentially supports the structural perception of unstructured text @xcite , being an important step for natural language understanding (nlu). more specifically, entity typing has a broad impact on various nlp tasks that depend on type understanding, including coreference resolution @xcite , entity linking @xcite ; tianran implies good predictions by exploiting spurious correlations and indicates bad predictions when spurious correlations no longer exist. mlmet falsely relies on the entity name to give "island" predictions for a hotel mention, incorrectly infers types of the dependent "car" rather than the headword "spoiler", and gives only the coarse label "animal" with more fine-grained missing. et al., 2021), entity disambiguation @xcite , event detection @xcite and relation extraction @xcite . to tackle the task, literature has developed vari-ous predictive methods to capture the association between the contextualized entity mention representation and the type label. for instance, a number of prior studies approach the problem as multiclass classification based on distinct ways of representing the entity mentioning sentences @xcite @xcite . other studies formulate the problem as structured prediction and leverage structural representations such as box embeddings @xcite and causal chains @xcite to model the dependency of type labels. however, due to shortcuts from surface patterns to annotated entity labels and biased training, existing entity typing models are subject to the problem of spurious correlation @xcite @xcite . for example, given a sentence "last week i stayed in treasure island for two nights when visiting las vegas.", a sota model like mlmet @xcite may overly rely on the entity name and falsely type treasure island as an island, while ignoring the sentential context that indicates this entity as a resort or a hotel. for morphologically rich mentions with multiple noun words such as "most car spoilers", entity models may fail to understand its syntactic structure and miss the target entity from the actual head-dependent relationship, leading to predictions describing the dependent car (car, vehicle) rather than the head spoilers (part). such spurious clues can cause the models to give unfaithful entity typing and further harm the machine's understanding of the entity mentioning text. to comprehensively investigate the faithfulness and reliability of entity typing methods, the first contribution of this paper is to systematically define distinct kinds of model biases that are reflected mainly from spurious correlations. particularly, we identify the following six types of existing model biases, for which examples are illustrated in fig. 1 . those biases include mention-context biases, lexical overlapping biases, named entity biases, pronoun biases, dependency structure biases and overgeneralization biases. we provide a prompt-based method to identify instances posing those biases to the typing model. in the meantime, we illustrate that common existence of these types of biased instances causes it hard to evaluate whether a model is faithfully comprehending the entire context to infer the type, or trivially leveraging surface forms or distributional cues to guess the type. we introduce a counterfactual data augmentation @xcite ) method for debiasing entity typing, as the second contribution of this paper. given biased features, we reformulate entity typing as a type-querying cloze test and leverage a pre-trained language model (plm) to fill in the blank. by augmenting the original training set with their debiased counterparts, models are forced to fully comprehend the sentence and discover the fundamental cues for entity typing, rather than rely on spurious correlations for shortcuts. compared with existing debiasing approaches such as product of experts @xcite , focal loss (karimi mahabadi et al., 2020), contrastive learning @xcite and counterfactual inference @xcite , our counterfactual data augmentation approach helps improve generalization of all studied models with consistently better performance on both original ufet @xcite and debiased test sets. ## method in this section, we start with the problem definition ( §2.1) and then categorize and diagnose the spurious correlations causing shortcut predictions by the typing model ( §2.2). lastly, we propose a counterfactual data augmentation approach to mitigate the identified spurious correlations, as well as several alternative techniques that apply ( §2.3). ## experiments in this section, we start with describing the experimental setups ( §3.1). next, we diagnose entity models to measure their reliance on spurious correlations ( §3.2). we then compare our counterfactual data augmentation with other debiasing techniques for spurious correlation mitigation ( §3.3). ## related work entity typing earlier studies on entity typing @xcite @xcite learned contextual embeddings for entity mentions and types to capture their association. to model label correlations without annotated label hierarchies in ufet, labelgcn @xcite introduced the graph propagation layer to encode global label co-occurrence statistics and their word-level similarities, whereas hmgcn @xcite proposed to infer this information from a knowledge base. for the same purpose, approach bilstm mlmet box4types @xcite was proposed to embed concepts as hyper rectangles (boxes), such that their topological relations can represent type hierarchies. considering the prevailing noisy labels in existing entity typing datasets, @xcite performed supervised denoising to filter and fix noisy training labels. @xcite introduced distant supervision from entity linking results. to tackle the sparsity of training, recent work conducted data augmentation with a masked language model and wordnet knowledge to enrich the training data @xcite , and made use indirect supervision from natural language inference @xcite . despite much attention in literature, to the best of our knowledge, our work represents the first investigation on faithfulness and reducing shortcuts in this task. ## conclusions to comprehensively investigate the faithfulness and reliability of entity typing methods, we systematically define six kinds of model biases that are reflected mainly from spurious correlations. in addition to diagnosing the biases on representative models using benchmark data, we also present a counterfactual data augmentation approach that helps improve the generalization of different entity typing models with consistently better performance on both original and debiased test sets.