A mbi C oref: Evaluating Human and Model Sensitivity to Ambiguous Coreference (2023)

Abstract: Given a sentence “Abby told Brittney that she upset Courtney”, one would struggle to understand who “she” refers to, and ask for clarification. However, if the word “upset” were replaced with “hugged”, “she” unambiguously refers to Abby. We study if modern coreference resolution models are sensitive to such pronominal ambiguity. To this end, we construct AmbiCoref, a diagnostic corpus of minimal sentence pairs with ambiguous and unambiguous referents. Our examples generalize psycholinguistic studies of human perception of ambiguity around particular arrangements of verbs and their arguments. Analysis shows that (1) humans are less sure of referents in ambiguous AmbiCoref examples than unambiguous ones, and (2) most coreference models show little difference in output between ambiguous and unambiguous pairs. We release AmbiCoref as a diagnostic corpus for testing whether models treat ambiguity similarly to humans.

## introduction ambiguity is a fundamental feature of language @xcite that some linguists believe arises because of a pressure for efficient communication @xcite . recently, several works have highlighted the existence of ambiguity in tasks such as question answering @xcite , frame disambiguation @xcite , anaphora resolution @xcite and language modeling @xcite . yet systematic evaluation of how models react to ambiguity across many types of language processing problems is missing. we contribute one such study about coreference resolution. coreference resolution is crucial to natural language understanding, especially in long contexts, such as dialog. ambiguity may arise naturally 1 our dataset and code is available at https://github.com/lucyyyw/ambicoref . in dialog, but existing models do not have welldefined target behavior for such coreferences. in contrast, when people encounter coreferential ambiguity, they recognize it, and can ask for clarification. existing resources, such as ontonotes @xcite , do not provide fine-grained annotations of such instances to evaluate model behavior. this may result in models not being calibrated to handle the uncertainty in interpretations of ambiguous statements. in this work, we ask how sensitive to ambiguity are models trained on these resources? to understand how existing coreference models react to ambiguity, we construct a diagnostic corpus, ambicoref. ambicoref is composed of minimal pairs with ambiguous and unambiguous referents, created from four types of templates. ambiguity is achieved by reducing context sizes to one sentence, and creating sentences where participating verbs under-constrain the interpretation of their arguments. for example, in we verify that humans perceive instances in ambicoref in intended ways by crowdsourcing judgements ( §3). annotators are asked to find the coreferent for a pronoun in a sentence, and rate their confidence, to account for the gradience in ambiguity judgements @xcite . we find that, for unambiguous instances, humans strongly associate the pronoun with the intended noun but for ambiguous ones, they show reduced confidence across all templates, where the majority of participants are either not confident or mark them as ambiguous. this suggests that humans process ambiguous and unambiguous sentences in ambi-coref in qualitatively different ways. ambicoref can be used to evaluate model behavior in the presence of ambiguity. we analyze five representative english models: three in corenlp @xcite , spanbert @xcite , and neuralcoref 4.0 @xcite ) ( §4). our main evaluation involves comparing coreference cluster assignments of the pronoun, between ambiguous and unambiguous samples. 4 out of the 5 models we analyze show almost no behavioral change. unlike humans, coreference models largely do not alter their decisions in the presence of ambiguity. our analysis implies models likely need to explicitly account for ambiguity to achieve human-like behavior in the face of ambiguous input. ## dataset construction to understand model sensitivity towards coreferential ambiguity, we build ambicoref using four types of templates, shown in ## human judgements the templates used to create ambicoref generalize several psycholinguistic studies using lexical resources. next, we verify that humans perceive ambiguity in these examples in the intended ways. we extract a subset of data for each template and ask amazon mechanical turk workers which person a pronoun refers to (marked as a or b in ## model evaluation we now examine if we can detect sensitivity to ambiguity in existing coreference resolution models by evaluating on ambicoref. we experiment 6 with five representative models: neu-ralcoref 4.0 model from hugging face 7 , span-bert @xcite ) representation within the independent framework for end-to-end coreference @xcite , and the three models in stanford corenlp @xcite : deterministic @xcite , statistical @xcite and neural mention ranking @xcite . all models were trained on the conll 2012 dataset @xcite . here, we evaluate the model's final predictions, not their distribution over possible choices. the reason is two-fold: (1) not all models produce a distribution and (2) initial analysis revealed that the models are miscalibrated, as in other settings @xcite , making it unreliable to interpret their output scores directly. ## discussion and conclusion overall, our results suggest that model behavior significantly deviates from how human treat ambiguous coreference. we lend more evidence that models miss aspects of how people understand language, especially in discourse @xcite . the reason is likely in part that models are trained on resources which do not account for distributions in judgments. as a result, models do not have well-defined behavior when ambiguity arises and are poorly calibrated. training models with finer-grained coreference judgments could allow models to better align with human behavior. techniques to improve model calibration could also be effective, allowing models to abstain or seek clarification when ambiguity arises. we hope that ambicoref can serve as a diagnostic set for future modeling approaches in evaluating their sensitivity to instances of ambiguity in language.