C oref P rompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities (2023)

Abstract: Event coreference resolution (ECR) aims to group event mentions referring to the same real-world event into clusters. Most previous studies adopt the “encoding first, then scoring” framework, making the coreference judgment rely on event encoding. Furthermore, current methods struggle to leverage human-summarized ECR rules, e.g., coreferential events should have the same event type, to guide the model. To address these two issues, we propose a prompt-based approach, CorefPrompt, to transform ECR into a cloze-style MLM (masked language model) task. This allows for simultaneous event modeling and coreference discrimination within a single template, with a fully shared context. In addition, we introduce two auxiliary prompt tasks, event-type compatibility and argument compatibility, to explicitly demonstrate the reasoning process of ECR, which helps the model make final predictions. Experimental results show that our method CorefPrompt performs well in a state-of-the-art (SOTA) benchmark.

## introduction within-document event coreference resolution (ecr) task aims to cluster event mentions (i.e., triggers that most clearly indicate the occurrences of events) in a document such that all event mentions in the same cluster refer to the same real-world event. consider the following example: {former pakistani dancing girl} arg 1 commits {suicide} ev 1 12 years after horrific {acid} arg 2 {attack} ev 2 which {left} ev 3 {her} arg 3 looking "not human". {she} arg 4 had undergone 39 separate surgeries to repair {damage} ev 4 . leapt to {her} arg 5 {death} ev 5 from {sixth floor rome building} arg 6 {earlier this month} arg 7 . {her ex-husband} arg 8 was {charged} ev 6 with {attempted murder} arg 9 in {2002} arg 10 but has since been {acquitted} ev 7 . this example contains seven event mentions (ev 1 -ev 7 ) and ten entity mentions (arg 1 -arg 10 ) that serve as arguments. among them, the death event mention ev 1 with the argument arg 1 and the death event mention ev 5 with the arguments arg 5 , arg 6 , and arg 7 are coreferential, as both of them describe the girl's suicide by jumping off a building; the injury event mention ev 3 with the arguments arg 2 and arg 3 and the injury event ev 4 with the argument arg 4 are coreferential, as both of them describe the girl's disfigurement; other event mentions are singletons. identifying the coreference between event mentions is essential for understanding the content of the text, and is the key to aggregating information that can support many downstream nlp applications, such as event extraction @xcite , discourse analysis @xcite and timeline summarization @xcite . event coreference resolution is more challenging than entity coreference resolution due to the complex event structure @xcite , triggers and corresponding arguments are loosely distributed in the text, which needs to consider the compatibilities of multiple event dimensions, including triggers, arguments, event types, etc. most previous work regards ecr as an event-pair classification task @xcite @xcite , i.e., judging whether two event mentions are coreferential. early neural methods focus on obtaining trigger representations by various encoders and then manually constructing matching features @xcite , while recent studies integrate event compatibility into judgments using well-designed model structures @xcite @xcite or directly incorporate argument information into event modeling @xcite , alleviating noise brought by wrongly extracted or empty event slots. other work @xcite learns ecr-aware event representations through contrastive learning or multi-level modeling. however, at least two limitations exist in the above studies. first, most current ecr studies adopt the "encoding first, then scoring" framework, wherein they first use encoders (e.g., bert) to encode the text and obtain event mention embeddings, and then apply a scorer to evaluate the coreference score of event pairs based on these learned embeddings. this results in the "information blocking" issue. essentially, since the scorer solely utilizes the learned embeddings as inputs, almost the entire coreference determination relies on the event encoding. however, the event encoding is performed independently, without direct influence from coreference judgment. as a result, the encoder may not accurately capture contextual information that is crucial for ecr. especially for coreferential event pairs with unbalanced information, the learned embeddings may significantly differ if one event has rich arguments while the other has only a vague trigger. to alleviate this problem, @xcite and @xcite constrain event modeling by attraction and repulsion losses, making coreferential events have similar representations. however, this approach still performs event modeling and coreference discrimination separately, and @xcite find that appropriate tensor matching can achieve similar effects. obviously, event modeling and coreference judgment are closely associated, and even humans need to review the original text to capture detailed clues when judging coreference. to address this issue, we convert ecr into a mask language prediction task using a well-designed prompt, thus the simultaneously performed event modeling and coreference judgment can interact conveniently based on a fully shared context to improve each other. second, previous methods struggle to utilize human knowledge, e.g., coreferential events should have the same event type and compatible arguments, and often require designing unique model structures to guide the model to focus on the compatibility of event elements, e.g., strictly matching extracted event elements @xcite or softly integrating event compatibilities @xcite . these approaches rely on a large amount of training, and cannot guarantee that the model can finally capture the compatibility features or understand the association between compatibilities and coreference. in the worst cases, the automatically captured features focus on other aspects and fail to discover the association between compatibilities and coreference due to the bias of interaction features or noise in the data. in this paper, we introduce two auxiliary prompt tasks, event-type compatibility and argument compatibility, to explicitly demonstrate the inference process of coreference judgment in the template and guide the model to make final decisions based on these compatibilities. benefiting from prompting, our method can navigate the model's focus on event type and argument compatibilities using templates in natural language and can be adjusted to different compatibility levels with the assistance of soft label words. we summarize our contributions as follows: ## model formally, given a sample @xmath0, (ev i , ev j ) , where d is a document and (ev i , ev j ) is an event mention pair in d. an ecr model first needs to judge the label @xmath1, non-coref} for every event mention pair, and then organize all the events in d into clusters according to the predictions. therefore, the key lies in the coreference classification of event mention pairs. different from fine-tuning methods that classify based on learned event mention embeddings, the promptbased methods we adopt reformulate the problem as a mask language modeling task. ## analysis and discussion we first analyze the differences between promptbased and fine-tuning methods in processing the ecr task, showing the advantages of prompting with well-designed templates, and then discuss the contribution of each component in our approach through ablation experiments. ## conclusion in this paper, we design a specific prompt to transform ecr into a mask language prediction task. as such, event modeling and coreference judgment can be performed simultaneously based on a shared context. in addition, we introduce two auxiliary mask tasks, event-type compatibility and argument compatibility, to explicitly show the reasoning process of ecr, so that ptm can synthesize multidimensional event information to make the final predictions. experimental results on the kbp 2017 dataset show that our method performs comparably to previous sota, without introducing full-text level encoding. in future work, we will continue to study how to incorporate more human knowledge into the prompt-based approach for ecr.