Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions (2021)

Abstract: Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference. Capturing the uncertainty over each variable can be crucial for inference among multiple dependent variables. Previous work on joint coreference employs heuristic approaches, lacking well-defined objectives, and lacking modeling of uncertainty on each side of the joint problem. We present a new approach of joint coreference, including (1) a formal cost function inspired by Dasgupta’s cost for hierarchical clustering, and (2) a representation for uncertainty of clustering of event and entity mentions, again based on a hierarchical structure. We describe an alternating optimization method for inference that when clustering event mentions, considers the uncertainty of the clustering of entity mentions and vice-versa. We show that our proposed joint model provides empirical advantages over state-of-the-art independent and joint models.

## introduction coreference resolution refers to the task of detecting mentions of various entities and events and identifying groups of mentions referring to the same real-world entity or event. it is a fundamental nlp task that has several downstream applications such as question answering @xcite , textual entailment @xcite , building and maintaining kbs @xcite , and multi-document summarization @xcite . often these downstream applications consume a set of documents, and thus require detection of coreference relations between event and entity mentions spread across documents such as multiple news articles. event and entity coreference decisions often have rich dependencies on each other. consider the example in figure 1 where a system performing 1.) @xcite e1 landsv1 green datacenter consultant [eyp]e2. ## related work entity coreference resolution, especially in the within-document setting has seen a tremendous amount of improvement over recent years. early work on entity coreference used hand-crafted syntactic and semantic features @xcite @xcite while recent top-performing models are neural models that perform mention detection, followed by mention clustering in an end-to-end fashion @xcite @xcite . event coreference has also seen similar trends with early work using lexical features such as wordnet synsets, head lemma of the verb @xcite while more recent work uses event embeddings from pre-trained word embeddings or pretrained language models @xcite @xcite . while the majority of the work on event or entity coreference attempts to solve the tasks separately, some prior work does exploit the argument-predicate structure to derive additional features for enriching entity/event representations. in this work, we go a step further and perform joint clustering of event and entity mentions instead of merely using event or entity mentions to derive additional lexical features. prior work that jointly predicts event and entity coreference decisions does so at a flat clustering level @xcite . the joint clustering method incrementally merges entity or event clusters, and computes merge score between a pair of entity (or event) clusters based on a learned similarity function that incorporates features from the involved mentions as well as clusters of related event (or entity) mentions. however, they use flat clustering of mentions at the given time step to compute merge scores in the next time step. in this work, instead of using an incrementally built flat clustering, we use cluster trees over entity and event mentions that help encode uncertainty over coreference decisions as part of our joint inference. ## joint coreference model problem definition the task of coreference resolution is a clustering problem where data points are textual spans of event (or entity) mentions and clusters refer to the real-world events (or entities). in this work, we focus on cross-document coreference foot_0 in which the entity and event mentions participating in the clustering problem come from a corpus of documents. let @xmath0, . . . , d n d } be a set of n d documents containing a set of event mentions m v and a set of entity mentions m e with ground-truth flat clustering e * our approach for joint event and entity coreference uses dependencies between event and entity mentions ( § 3.2). we formalize a cost function that uses an independently trained pairwise similarity ( § 3.4) along with relational similarity ( § 3.5) defined using these dependencies in a way that captures uncertainty over coreference decisions. finally, we describe our joint inference procedure to optimize the proposed cost function in § 3.7. ## experiments dataset we run experiments on the ecb+ corpus which consists of both within-and cross-document coreference resolution for both entity and event mentions. ecb+ corpus consists of a set of news articles, grouped under topics. each topic consists of two sub-topics containing articles about two different real-world events which use similar vocabulary, thus posing a challenge for coreference systems. for example, the first two sentences in figure 1 are from the same subtopic containing documents of hp's acquisition of eyp while the third sentence comes from a document from a different subtopic which talks about hp's acquisition of eds. this presents a challenging setting when evaluating cross-document coreference at the topic level as there are instances of coreferent event and entity mentions with divergent surfaces forms as well as mentions of different real-world events and entities with similar surface forms. we show statistics on ecb+ in