Coreference resolution is an important task for natural language understanding, and the resolution of ambiguous pronouns a longstanding challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in sufficient volume or diversity to accurately indicate the practical utility of models. Furthermore, we find gender bias in existing corpora and systems favoring masculine entities. To address this, we present and release GAP, a gender-balanced labeled corpus of 8,908 ambiguous pronoun–name pairs sampled to provide diverse coverage of challenges posed by real-world text. We explore a range of baselines that demonstrate the complexity of the challenge, the best achieving just 66.9% F1. We show that syntactic structure and continuous neural models provide promising, complementary cues for approaching the challenge.

A discourse typically involves numerous entities, but few are mentioned more than once. Distinguishing those that die out after just one mention (singleton) from those that lead longer lives (coreferent) would dramatically simplify the hypothesis space for coreference resolution models, leading to increased performance. To realize these gains, we build a classifier for predicting the singleton/coreferent distinction. The model’s feature representations synthesize linguistic insights about the factors affecting discourse entity lifespans (especially negation, modality, and attitude predication) with existing results about the benefits of “surface” (part-of-speech and n-gram-based) features for coreference resolution. The model is effective in its own right, and the feature representations help to identify the anchor phrases in bridging anaphora as well. Furthermore, incorporating the model into two very different state-of-the-art coreference resolution systems, one rule-based and the other learning-based, yields significant performance improvements.

Abstract This paper addresses the current state of coreference resolution evaluation, in which different measures (notably, MUC, B 3 , CEAF, and ACE-value) are applied in different studies. None of them is fully adequate, and their measures are not commensurate. We enumerate the desiderata for a coreference scoring measure, discuss the strong and weak points of the existing measures, and propose the BiLateral Assessment of Noun-Phrase Coreference, a variation of the Rand index created to suit the coreference task. The BiLateral Assessment of Noun-Phrase Coreference rewards both coreference and non-coreference links by averaging the F-scores of the two types, does not ignore singletons – the main problem with the MUC score – and does not inflate the score in their presence – a problem with the B 3 and CEAF scores. In addition, its fine granularity is consistent over the whole range of scores and affords better discrimination between systems.

By providing a better understanding of paraphrase and coreference in terms of similarities and differences in their linguistic nature, this article delimits what the focus of paraphrase extraction and coreference resolution tasks should be, and to what extent they can help each other. We argue for the relevance of this discussion to Natural Language Processing.