Cross-Document Event Coreference Resolution on Discourse Structure (2023)

Abstract: Cross-document event coreference resolution (CD-ECR) is a task of clustering event mentions across multiple documents that refer to the same real-world events. Previous studies usually model the CD-ECR task as a pairwise similarity comparison problem by using different event mention features, and consider the highly similar event mention pairs in the same cluster as coreferent. In general, most of them only consider the local context of event mentions and ignore their implicit global information, thus failing to capture the interactions of long-distance event mentions. To address the above issue, we regard discourse structure as global information to further improve CD-ECR. First, we use a discourse rhetorical structure constructor to construct tree structures to represent documents. Then, we obtain shortest dependency paths from the tree structures to represent interactions between event mention pairs. Finally, we feed the above information to a multi-layer perceptron to capture the similarities of event mention pairs for resolving coreferent events. Experimental results on the ECB+ dataset show that our proposed model outperforms several baselines and achieves the competitive performance with the start-of-the-art baselines.

## introduction in real-world texts, there are usually a large number of sentences that describe the same event in reality, and an event will be mentioned repeatedly in multiple documents. when multiple event mentions (an instance of a specific event in texts) point to the same event ontology, these event mentions are coreferent. event coreference resolution is useful for many natural language processing (nlp) applications, such as information extraction @xcite , topic detection @xcite , and question answering @xcite . depending on whether the event mentions are in the same document, this task can be further divided into within-document (wd-ecr) and cross-document (cd-ecr) event coreference resolution. this paper focuses on the cross-document task cd-ecr. events mainly consist of triggers and arguments. since triggers are the main words that can most clearly express the occurrence of events, each event can be represented as its corresponding trigger. consider the following two event mentions as examples: s1: the court would hand down a ruling on whether the former president will remain detained for three more months before the current extension expires. s2: the former president detainment was previously extended for three months in july. the event triggers in the event sentences s1 and s2 are "detained" and "detainment", respectively. although these two triggers have different forms, they both refer to the same judicial type arrest. therefore, "detained" in s1 and "detainment" in s2 have a coreference relationship and can be aggregated to form a coreferent chain. previous studies on wd-ecr or cd-ecr typically took a pair of event mentions with their event sentences as input and then predicted whether they are coreferent using a binary classifier @xcite @xcite . to this end, an important step in their models was to extract discriminative features of event mentions and then used encoding method to represent event mentions as vectors. simultaneously, almost all previous studies considered the task of event coreference resolution as a similarity model and focused on how to calculate the feature similarities between event mention pairs @xcite . besides, some studies also focused on data augmentation @xcite @xcite @xcite , which boost event coreference resolution on additional raw data. in the cd-ecr task, there is a limitation that two event mentions in a pair may be scattered across one or more documents and have a long distance. previous studies on both cd-ecr and wd-ecr have attempted to extract the context features, which only represented event mentions from the local perspective @xcite and the information from the global perspective was neglected. to address the above issue, we introduce discourse structure to extract global information of event mentions and then apply them to cd-ecr. discourse structure is a tree structure (called "discourse tree" below). generally, discourse tree is constructed by discourse rhetorical structure @xcite (drs) constructor, which aims to represent input text as a tree structure like the example in figure 1 , where the leaf nodes are elementary discourse units (edu) and it has smaller fine-grained units than event sentences. the discourse tree can clearly express the rhetorical relation and nuclearity relation (e.g., ns-summary) of each edu, which can provide useful information for associating those long-distance event mention pairs. on the other hand, the drs constructor generally performs tree construction on single document. in the cd-ecr task, if two event mentions in the same document, we can directly input the document to drs constructor to obtain discourse tree, and then extract the global information of the two event mentions. however, if the query is a crossdocument event mention pair, and the models send their documents to the drs constructor separately, the global information of this event mention pair cannot be obtained directly. therefore, we also propose a construction strategy of cross-document discourse tree, which is helpful to obtain the global information of cross-document event mention pairs. we summarize the contributions of our work as follows. ## related work research on event coreference resolution mainly draws on the method of entity coreference resolution, which aims to resolve noun phrases/mentions for entities @xcite @xcite @xcite . since event mentions have more complex structures than entity mentions, event coreference resolution is a more challenging task than entity coreference resolution @xcite . early research on event coreference resolution mainly applied machine learning methods @xcite @xcite . some researchers incorporated various kinds of regular methods into machine learning methods and improved the performance of event coreference resolution @xcite @xcite . since these methods relied heavily on manual annotation features, some studies paid more attention to raw text event coreference resolution @xcite @xcite @xcite . recently, deep learning work has been applied to both wd-ecr @xcite @xcite @xcite @xcite and cd-ecr @xcite @xcite @xcite . in wd-ecr, @xcite used convolutional neural networks to mine event features for event coreference resolution. huang et al. (2019) incorporated argument compatibility knowledge from a large number of the unlabeled corpus. lu and ng (2021) investigated span-based models for event coreference resolution. in cd-ecr, argument information was introduced into event representations ... roberta edu embeddings e i e j t i t j e i ○ e j t i ○ t j drs constructor lcp dt-sdp bi-lstm r lcp r dt-sdp mlp pairwise score inputing documents edu sequence local information representation global information representation event coreference prediction r global (i, j) r local (i, j) probability distribution figure 2: the overall framework of our cd-ecr model. (barhom et al., 2019; zeng et al., 2020; yu et al., 2022), barhom et al. (2019) jointly learned entity and event coreference resolution and leveraged predicate-argument structures. zeng et al. (2020) integrated event-specific paraphrases and argumentaware semantic embeddings for cd-ecr. yu et al. (2022) augmented pairwise representation with structured argument features to improve cd-ecr performance. caciularu et al. (2021) pretrained a cross-document language model via sets of related documents for cd-ecr. held et al. (2021) extracted event mentions features from the local perspective and trained a fine-grained classifier to improve cd-ecr performance. compared with previous deep learning work for cd-ecr, we present a novel global information representation for event mentions to enhance the interaction between long-distance event mentions. ## model formally, given a set of documents @xmath0, d 2 , ..., d |d| }, whose element d i consists of a series of sentences {s i1 , s i2 , ..., s i|d i | }}, and a set of event mentions @xmath1, m 2 , ..., m |m |} , these event mentions in m can be distributed in different documents in d. cd-ecr aims to discover the event mentions in m from different documents in d that refer to the same event ontology in the real world and gather them into the same cluster. ecb+ is the most popular corpus for cd-ecr and our work is also performed on this corpus. figure 2 shows an overview of our cd-ecr model, which includes three main components: 1) local information representation (lir) to obtain local perspective information of event mentions from edu embeddings, 2) global information representation (gir) to extract global information representation from the discourse tree constructed by the drs constructor, 3) event coreference prediction (ecp) to receive the global and local representations of event mention pairs and predict the probability that two event mentions are coreferent. ## conclusion in this paper, we propose a cross-document event coreference resolution model. the novelty of our model is twofold. first, we introduce the discourse structure that represents global information for pairs of event mentions to provide support for event coreference resolution. second, we propose a strategy for constructing cross-document discourse trees, which allows cross-document coreferent event mentions to be easily identified by the model. experimental results on the ecb+ dataset show that our proposed model outperforms several baselines. in the future, we plan to extend our task to cross-modal event coreference resolution.