What happens before and after: Multi-Event Commonsense in Event Coreference Resolution (2023)

Abstract: Event coreference models cluster event mentions pertaining to the same real-world event. Recent models rely on contextualized representations to recognize coreference among lexically or contextually similar mentions. However, models typically fail to leverage commonsense inferences, which is particularly limiting for resolving lexically-divergent mentions. We propose a model that extends event mentions with temporal commonsense inferences. Given a complex sentence with multiple events, e.g., “the man killed his wife and got arrested”, with the target event “arrested”, our model generates plausible events that happen before the target event – such as “the police arrived”, and after it, such as “he was sentenced”. We show that incorporating such inferences into an existing event coreference model improves its performance, and we analyze the coreferences in which such temporal knowledge is required.

## introduction the goal of cross-document event coreference resolution is to determine if various event mentions (e.g. shot, gunshot), across one or more documents, refer to the same event. existing systems represent each mention within its context using a language model @xcite , and train a scorer to predict if two mentions corefer, based on their lexical and contextual similarity. while many coreferring mention pairs in event coreference datasets such as ecb+ @xcite are lexically and contextually similar, or even share the same lemma @xcite , the difficulty arises for dissimilar coreferring mentions. for example, in figure 1 , spent and hospitalized are coreferring. these mentions are not lexically similar, and are not often used in similar contexts. in this paper, we improve the ability of existing cross-document event coreference systems to resolve such challenging coreferring mentions, by providing additional context in the form of commonsense knowledge. we focus on two temporal commonsense relations -before and after -pertaining to typical events that happen before and after the target event. for instance, in figure 1 , we may infer that before dalton was shot, a shooter loaded their gun. similarly, we may infer that dalton was hurt prior to his hospitalization and got discharged afterward. our first contribution is the development of a commonsense reasoning engine that can reason about these two temporal relations. existing commonsense models @xcite ) may generate such inferences for simple sentences with a single event, such as "bryant dalton was shot", but they do not support complex sentences with multiple events of interest (e.g. shot, hospitalized). further, they may conflate the inferences for different events. we develop a multievent commonsense model that considers the entire context and is capable of generating separate inferences for each target event in complex sentences. as an additional contribution, we incorporate the inferences into the pairwise mention scorer of a cross-document event coreference system @xcite . we produce before and after inferences for each event mention. we then embed the inferences, either by attending each mention to its own inferences (intra-span) or to the other mention's inferences (inter-span). the results confirm that commonsense inferences are useful for event coreference. each of our model variants improves upon the baseline performance, with the intra-span version performing the best. we further analyze the successful predictions and interpret how the commonsense inferences help resolve difficult mention pairs. in the future, we plan to extend our multi-event inference engine to additional commonsense knowledge types and apply it to other discourse tasks, such as summarization, dialogue, and story comprehension. 1 ## background in this work, we improve the performance of a system for cross-document coreference resolution by incorporating temporal commonsense inferences pertaining to the events. we first provide background on event coreference resolution ( §2.1). we then describe related work concerning event-centered commonsense ( §2.2), along with approaches for using language models for data augmentation ( §2.3). ## method the architecture of our method is shown in figure 1 . we use the same clustering method as in @xcite but revise the pairwise scorer. our goal is to improve the model's ability to resolve coreferences between mention pairs that are not lexically or contextually similar, but where one mention could be inferred from the other using commonsense knowledge and reasoning. thus, we develop a commonsense inference engine (sec 3.1) instructions: read the context sentence and write at least two inferences for question 1 and two inferences for question 2. as shown in the examples, each inference is expected to be a short sentence between 5-10 words. context: a publicist says tara reid has checked herself into rehab. question 1: what typically happens before the event checked herself? question 2: what typically happens after the event checked herself? figure 2 : an example task on amazon mechanical turk. and use it to enhance the pairwise scorer (sec 3.2). ## evaluation we discuss the results on the event coreference task (sec 5.1), the validity of the commonsense inferences generated by our inference engine (sec 5.2), and present ablation tests (sec 5.3). ## analysis 6.1 attention scores figure 4 presents an example of a mention pair (drunken driving, dui) that was incorrectly predicted as non-coreferring by the baseline and correctly predicted as coreferring by the intra-span model. the inferences for each mention are sorted and highlighted according to their corresponding attention weights. the highest scoring before inference for the first mention, "jamal is drinking and driving", and the second inference for the second mention "wiliams drank alcohol", are similar, which likely contributed to recognizing the coreference. figure 5 similarly shows an example that was incorrectly predicted as non-coreferring by the baseline and correctly predicted as coreferring by the inter-span model. here, we can clearly observe the interplay between the second mention drove off and the inferences of the first mention hit related to driving and fleeing from the scene. the lexical and contextual diversity of these mentions necessitates commonsense inferences to resolve the coreference. appendix c provides a second set of examples. ## conclusions in this paper, we investigated the effect of injecting temporal commonsense knowledge in the task of event coreference resolution. by using eventspecific inferences generated by our commonsense model, we improve the performance of a baseline model. our analysis shows that the pairwise scorer attends to inferences that are beneficial in solving challenging coreferences. in the future, we plan to extend the multi-event commonsense model to additional relations, and to incorporate such knowledge into other discourse tasks.