Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization (2023)

Abstract: Document-level multi-event extraction aims to extract the structural information from a given document automatically. Most recent approaches usually involve two steps: (1) modeling entity interactions; (2) decoding entity interactions into events. However, such approaches ignore a global view of inter-dependency of multiple events. Moreover, an event is decoded by iteratively merging its related entities as arguments, which might suffer from error propagation and is computationally inefficient. In this paper, we propose an alternative approach for document-level multi-event extraction with event proxy nodes and Hausdorff distance minimization. The event proxy nodes, representing pseudo-events, are able to build connections with other event proxy nodes, essentially capturing global information. The Hausdorff distance makes it possible to compare the similarity between the set of predicted events and the set of ground-truth events. By directly minimizing Hausdorff distance, the model is trained towards the global optimum directly, which improves performance and reduces training time. Experimental results show that our model outperforms previous state-of-the-art method in F1-score on two datasets with only a fraction of training time.

## introduction event extraction aims to identify event triggers with certain types and extract their corresponding arguments from text. much research has been done on sentence-level event extraction @xcite @xcite . in recent years, there have been growing interests in tackling the more challenging task of document-level multievent extraction, where an event is represented by a cluster of arguments, which may scatter across multiple sentences in a document. also, multiple events in the same document may share some common entities. for example, as shown in figure 1 , the two events, equity pledge and equity freeze, have their arguments scattered across the document. the same entity mentions, yexiang investment management co., ltd. and 13.07%, are involved in both events, with the former taking different argument roles @xcite , while the latter having the same argument role ('total holding ratio'). in such a setup, an event is not associated with a specific event trigger word or phrase, as opposed to the common setup in sentence-level event extraction. these challenges make it difficult to distinguish various events and link entities to event-specific argument roles. document-level multi-event extraction can be typically formulated as a table-filling task that fills the correct entities into a pre-defined event schema as shown in figure 1 . here, an event is essentially represented by a cluster of arguments. existing approaches @xcite @xcite @xcite usually involve two steps: (1) first model the entity interactions based on contextual representations; (2) then design a decoding strategy to decode the entity interactions into events and arguments. for example, @xcite and @xcite transformed this task into sequential path-expanding sub-tasks. each sub-task expands a path sequentially by gradually merging entities in a pre-defined order of event argument roles. the aforementioned approaches suffer from the following limitations: (1) they decode events from entity information and tend to produce local optimal results without considering the interdependency of multiple events globally in a document. (2) event decoding by iteratively merging entities suffers from error propagation that an event type or an entity that has been incorrectly classified cannot be corrected later. (3) every decoding decision requires iterating all entity mentions in a document, which is computationally inefficient. to address the above limitations, we propose an alternative approach for document-level multievent extraction with event proxy nodes and hausdorff distance minimization, named as proxy nodes clustering network (procnet). the event proxy nodes aim to capture the global information among events in a document. the hausdorff distance makes it possible to optimize the training loss defined as the difference between the generated events and the gold standard event annotations directly. this is more efficient compared to existing decoding approaches. our method involves two main steps: event representation learning and hausdorff distance minimization. for event representation learning, we create a number of proxy nodes, each of which represents a pseudo-event, and build a graph to update proxy nodes. entities mentioned in text are treated as nodes connecting to the proxy nodes. all the proxy nodes are interconnected to allow information exchange among the potential events. we employ a hypernetwork graph neural network (gnn) @xcite for updating proxy node representations. after event representation learning, each proxy node essentially resides in a new event-level metric space by aggregating informa-tion from the entity-level space. for hausdorff distance minimization, we regard the predicted events as a set and the ground-truth events as another set, and compute the hausdorff distance between these two sets, which simultaneously consider all events and all their arguments. we then minimize the hausdorff distance via gradient descent, where the model is trained to directly produce a globally optimal solution without the need of using decoding strategies as in existing approaches. in this way, our model learns globally and does not suffer from the problem of existing approaches that decode events based on local entity information. each entity is linked to every proxy node, and the association between an entity and a proxy node is updated at each training iteration. as such, our model avoids the error propagation problem caused by the iterative decoding strategy. in addition, our approach naturally addresses the problem that the same entity mention may be involved in multiple events since the entity will be mapped to a different event-level metric space depending on its associated proxy node. moreover, as our approach replaces iterative computation in decoding with parallel computation, it is computationally more efficient compared to existing path-expansion approaches, as will be shown in our experiments section. in summary, our main contributions are: early research on event extraction (ee) largely focused on sentence-level event extraction (see), aiming to classify the event trigger and arguments in a sentence. chen et al. (2015) decomposes see 𝒔 𝟏 𝒆 𝟏 𝒆 𝟑 𝒆 𝟐 𝒔 𝟐 𝒛 𝟐 𝒛 𝟏 november 15, 2016 november 14, 2016 proxy node #2 proxy node #1 𝒔 𝟑 ... proxy node decoding predicted event #1 𝑐 ! , {(𝑒 " , 𝑎 !," )} predicted event #2 𝑐 @xmath0," )} yexiang investment ltd context nodes for entities ... event representation learning with hypernetwork gnn ... event decoding 3 ... the receipt from the company's shareholder, 𝒆 𝟏 :yexiang investment management co., ltd. on the evening of 𝒆 𝟐 :november 15, 2016 regarding the notice of the shares being frozen. ... on 𝒆 𝟑 :november 14, 2016, yexiang investment received the notice of litigation preservation from the 𝒆 𝟒 :people's court of binjiang district, and granted a total of 𝒆 𝟓 :47,577,481 shares held by yexiang investment will be frozen, and the freezing period is from 𝒆 𝟔 :october 31, 2016 to 𝒆 𝟕 :october 30, 2019 ... yexiang investment is … holding 𝒆 𝟓 ':47,577,481 shares of the company, accounting for 𝒆 𝟖 :13.07% of the company's total share capital. ... on 𝒆 𝟗 :february 2, 2016, ... event type 𝑐 $ : equity pledge pledger: yexiang investment ltd totalholdingshares: 47,577,481 shares totalholdingratio: 13.07% … event type 𝑐 % : equity freeze pledger: yexiang investment ltd. frozeshares: 47,577,481 shares startdate: october 31, 2016 … ... entity extraction and representation learning ground-truth event #1 and #2 set of ground-truth events set of predicted events 𝒛 𝒏 proxy node #𝑛 hausdorff distance minimization 2 1 4 hausdorff distance 𝐷 . (. ) distance 𝑑(. ) figure 2 : overview of procnet with the example in figure 1 , where entity 1, 5, 8, 9 are arguments of event #1; entity 1, 4, 5, 6, 7, 8 are arguments of event #2; entity 2, 3 do not belong to any events. before training, proxy node embeddings are randomly initialized. entities are first mapped to entity representations in the entity-level space by entity representation learning. then in event representation learning, a hypernetwork heterogeneous graph is constructed with entity and context nodes connected with proxy nodes, and proxy nodes interconnected with each other. proxy nodes are updated to represent pseudo-events. afterwards, the proxy nodes and entity nodes are decoded into events, each of which is represented by an event types and a set of argument role-entity pairs in the event decoding step. finally, hausdorff distance minimization minimizes the distance between the set of predicted events and the set of ground-truth events to perform a global training in the new event-level metric space. compared to see, document-level event extraction (dee) appears to be more challenging. dee requires methods to model long-term dependencies among entities across multiple sentences. simply employing see approaches for dee may lead to incomplete and uninformative extractions @xcite . to address the problem, conditional generation have been proposed, which are conditioned on pre-specified templates or prompts @xcite @xcite . dee can also be formulated as a table-filling task where each event is represented as a cluster of arguments and an event type. in such a setup, it is usually not possible to associate a particular event trigger word or phrase with an event. @xcite proposed a key-event detection model. @xcite transformed event tables into a directed acyclic graph with path expansion. @xcite constructed a graph to build sentence communities. @xcite captured event clues as a series of intermediate results. @xcite constructed a heterogeneous gnn with a tracker mechanism for partially decoded events. @xcite modeled the relation between entities with relation-augmented attention transformer. these methods mainly focus on modeling entity inter-relations and rely on carefully-designed event decoding strategies. in contrast, we model events in the event-level metric space within a more global view and with less training time. ## conclusion in this paper, we no longer focus on inter-entities relation modeling and decoding strategy as in previous methods, but directly learns all events globally through the use of event proxy nodes and the minimization of the hausdorff distance in our proposed procnet. in our experiments, procnet outperforms state-of-the-art approaches while only requiring a fraction of time for training.