A Simple Unsupervised Approach for Coreference Resolution using Rule-based Weak Supervision (2022)

Abstract: Labeled data for the task of Coreference Resolution is a scarce resource, requiring significant human effort. While state-of-the-art coreference models rely on such data, we propose an approach that leverages an end-to-end neural model in settings where labeled data is unavailable. Specifically, using weak supervision, we transfer the linguistic knowledge encoded by Stanford?s rule-based coreference system to the end-to-end model, which jointly learns rich, contextualized span representations and coreference chains. Our experiments on the English OntoNotes corpus demonstrate that our approach effectively benefits from the noisy coreference supervision, producing an improvement over Stanford?s rule-based system (+3.7 F1) and outperforming the previous best unsupervised model (+0.9 F1). Additionally, we validate the efficacy of our method on two other datasets: PreCo and Litbank (+2.5 and +5 F1 on Stanford’s system, respectively).

## introduction coreference resolution is an important problem in language understanding. in the recent years, significant progress has been made on this task with coreference annotated corpora @xcite and deep neural network architectures @xcite @xcite . further gains have been obtained by leveraging contextualized text encoders like elmo @xcite , bert, spanbert, and longformer @xcite @xcite @xcite . the progress in supervised coreference resolution has not been accompanied by analogous improvements in unsupervised methods. the best performing work in this domain is the unsupervised mention-ranking systems proposed by @xcite . approaches that do not rely on gold annotation are highly desirable for this task, as coreference corpora are expensive to create. addressing this issue, weak supervision has been used for multilingual coreference resolution to automatically obtain labels for languages with no annotated datasets @xcite . in this paper, we introduce a simple yet effective approach for unsupervised coreference resolution, which leverages an end-to-end span-ranking coreference model @xcite and contextualized span representations. the end-to-end model is trained with weak supervision from stanford's coreference system @xcite , which, in turn uses a set of linguistic rules for coreference. previous works have used stanford system's rules as feature extractors @xcite @xcite . however, our approach uses stanford's rule-based sieves to produce noisy labels that are subsequently used to train the neural end-to-end resolver. the rationale behind the use of stanford's resolver for producing noisy labels lies in its ease of use and its modular structure, which allows us to interpret the value of the linguistic knowledge encoded in the system. linguists building a coreference resolver in a new domain can encode their prior knowledge via rules and improve the stanford system. our approach would further boost the resolver by incorporating pre-trained representations. nevertheless, our framework can be applied in combination with any method able to produce informative coreference labels. we assess our approach on three coreference corpora: english ontonotes @xcite , preco @xcite , and litbank @xcite . our experiments show that the imperfect information contained in the noisy labels can be effectively used to train the end-to-end model, producing an improvement over stanford's system. experimenting with different pre-trained language models, we observe that using bert boosts the performance of the end-to-end resolver. results further improve by using spanbert @xcite , which outperforms previous unsupervised models @xcite on the english ontonotes benchmark. we also evaluate the approach on two other coreference datasets: preco and litbank, and show strong gains over the stanford system. finally, we present a set of analyses that examine the information incorporated by weakly supervised training. ## method our approach relies on the c2f-coref end-to-end architecture proposed by @xcite , and on the classic rule-based stanford coreference system @xcite for the conll 2011 shared task @xcite . ## experiments we assess the proposed approach on three datasets: the english ontonotes v5.0 data from the conll-2012 shared task @xcite , preco @xcite , and litbank @xcite . we evaluate the c2f-coref model combined with different pre-trained language models (elmo, bert, and spanbert). these results are compared to the ones produced by stanford's system, in order to show the efficacy of the noisy supervision. moreover, we examine the performance of our weakly-supervised approach in contrast to two previous unsupervised models: multigraph @xcite and the em-based ranking model by @xcite . ## analysis performance on different types of coreference we investigate the capabilities of the weakly super ## conclusion we presented an approach for coreference resolution that, while being simple, effectively leverages the end-to-end span-ranking model in settings where labeled data is unavailable. experimental results highlight the efficacy of the weak supervision that the method is based upon, and showed performance gains over previous unsupervised systems.