Learning Event-aware Measures for Event Coreference Resolution (2023)

Abstract: Researchers are witnessing knowledge-inspired natural language processing shifts the focus from entity-level to event-level, whereas event coreference resolution is one of the core challenges. This paper proposes a novel model for within-document event coreference resolution. On the basis of event but not entity as before, our model learns and integrates multiple representations from both event alone and event pair. For the former, we introduce multiple linguistics-motivated event alone features for more discriminative event representations. For the latter, we consider multiple similarity measures to capture the distinction of event pair. Our proposed model achieves new state-of-the-art on the ACE 2005 benchmark, demonstrating the effectiveness of our proposed framework.

## introduction knowledge-inspired natural language processing (nlp) may be generally conducted on the basis of entities. however, researchers are realizing that it is the form of "event" that can more comprehensively depict the knowledge clues in nlp tasks @xcite , among which coreference resolution is a fundamental task for either entity or event. within-document event coreference resolution aims at finding all event mentions that refer to the same real-world event in a document @xcite . figure 1 shows an example of event coreference resolution from ace 2005 @xcite dataset. in the event coreference resolution, the trigger of an event mention usually refers to the word or phrase that describes the event with the most clarity and the event mention is typically the sentence containing the given trigger. as shown … attend his daughter ' s wedding{event1} ceremony … … to be taken{event2} home later in the afternoon to marry{event1} his eldest daughter … in the given example of figure 1 , the word taken triggers the transport event. the words wedding and marry trigger the marry event and these event mentions are coreferent because they both refer to the same real-world marry event. compared with entity-level coreference resolution, event coreference resolution is more challenging despite their similarity @xcite . the main reasons are: (1) an event contains more complex syntactic information than an entity; (2) event coreference resolution suffers from error propagation since it has more upstream tasks; (3) there are far less triggers than entities in a document. therefore, simply applying entity coreference resolution to event coreference resolution is unsatisfactory. many works utilize event linguistic features to address above challenges. in most cases, as shown in the above example, events that are coreferent have the same event features, such as type, polarity, and modality. therefore, many studies @xcite @xcite have suggested that incorporating event features into event coreference resolution is effective. despite of the developing trend of utilizing event features, the semantic similarity measures for event coreference resolution are less studied. most of the works simply used element-wise multiplication to measure the similarity between two spans @xcite @xcite for entity-level nlp tasks. however, incorporating multiple similarity measures interestingly shows helpful in many nlp tasks other than our concerned coreference resolution. for instance, @xcite proposed a deep pairwise word interaction model to measure semantic textual similarity between two text pieces, which directly utilized multiple similarity measures for answer selection task and semantic textual similarity measuring task. @xcite designed a u-shaped rewritten network to incorporate multiple similarity measures for context rewriting task. compared to entity-level coreference resolution, event-level coreference resolution focuses more on the cross-sentence semantic relationships, which are more complex and flexible. therefore, to better address the event coreference resolution problem, we need to measure the relationships between events in more diverse and comprehensive ways. inspired by this, in this paper, we propose a novel model for within-document event coreference resolution which integrates multiple measures from both event alone and event pair representations. as far as we know, we are the first to introduce more comprehensive and distinguishing measures for event-level coreference resolution. concretely, we introduce multiple linguistics-motivated event alone features for event alone representation and multiple similarity measures to capture the distinction of event pair. by conducting experiments on ace-2005 benchmark, our proposed model shows a significant improvement and achieves a new stateof-the-art f1 score of 61.71% in end-to-end settings and 92.32% in all gold settings. results demonstrate that our model can effectively models the event along features and event pair features, thus surpassing our baseline model and previous stateof-the-art models by large margins. ## related work entities serve as the fundamental basis for conducting various knowledge-inspired natural language processing (nlp) tasks @xcite @xcite . event coreference resolution, as an integral sub-task of natural language understanding, has gained increasing attention and finds applications in various domains. most recent works of event coreference resolution can be categorized into two types: (1) joint models; (2) pipeline models. in 2012, @xcite presented a joint model for cross-document coreference resolution. the model employed linear regression to build entity and event clusters and jointly solved events and entities references by handling both nominal and verbal mentions. araki and mitamura (2015) jointly formulated the event trigger extraction and event coreference as a problem of structured prediction to solve the error propagation problem. the authors utilized segment-based decoding with the multiplebeam search for event trigger identification and combined it with the best-first clustering for withindocument event coreference resolution. lu and ng (2017a) proposed a joint model of trigger detection, event coreference resolution, and event anaphoricity determination. the model employed a structured conditional random field with two types of factors: (1) unary factors; (2) binary and ternary factors. yu et al. (2020a) presented a pairwise representation learning scheme for cross-document and within-document event coreference problems. the scheme jointly encoded text snippets pair by forwarding concatenated sentences into a transformer encoder and employed structured argument features to argument the pairwise representation. different from these works, in this paper, we adopt a more straightforward pipeline model conforming to the general idea of the task. pipeline models mainly have two stages: event detection and event coreference resolution. traditionally, pipeline models first detect event triggers, arguments, and event features and then apply coreference resolution to the predicted event mentions. @xcite first used bidirectional gru to detect events and a logistic regression model to classify event features (i.e., realis). the model then employed the latent antecedent tree method to conduct coreference resolution. choubey and huang (2017) proposed a novel iterative approach for withindocument and cross-document event coreference resolution. the method constructs event clusters gradually by exploiting inter-dependencies among event mentions in two stages. @xcite employed oneie @xcite to extract event triggers and types. for other event features, the authors trained a simple classification model based on spanbert @xcite . the model then used a context-dependent gated module (cdgm) to incorporate event features for event coreference resolution and a noisy training method to tackle the error propagation problem. our work is closely related to @xcite . however, the method-ologies are different. in our model, we found that a simple ffnn achieves a better result than the gating mechanism which questions the necessity for the cdgm gating method in event coreference resolution. we also innovatively introduce multiple similarity measures for event pair representation. with different ablation experiments and training settings, we verified that our proposed multiple measures learning model is effective and we hope our results can offer a new insight into not only event coreference resolution but also other fundamental tasks in nlp. ## trigger detection and event alone feature classification our model uses oneie @xcite to detect event triggers and their types and designs a model to predict other event alone features. the overview of event alone features prediction model can be seen in figure 3 . specifically, we first insert special @xmath0. by doing so, we can transfer the original event feature prediction problem into a traditional text classification problem. to be more specific, we first use bert-large-cased @xcite to build contextualized representations h r for the input sentence x, which can be defined as: we then utilize mlp to classify each sentence. the probability distribution of event feature labels is calculated by: ## conclusion in this work, we propose a multiple measures learning model for event coreference resolution for the first time, as far as we know. by incorporating multiple event features and similarity measures, we are capable of calculating antecedent scores more comprehensively from different aspects. we unleash the potential of employing multiple similarity measures and filtering event features through various ablation experiments and proved that our method can effectively address the long sentence dependency problem. our model achieves an evident and significant improvement on the ace 2005 benchmark compared to current state-of-the-art models. in the future work, we aim at exploring the potential of incorporating multiple similarity metrics into different tasks.