Joint Event Extraction with Hierarchical Policy Network (2020)

Abstract: Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence. As a result, these efforts fail to utilize information interactions among event triggers, event arguments, and argument roles, which causes information redundancy. In view of this, we propose to exploit the role information of the arguments in an event and devise a Hierarchical Policy Network (HPNet) to perform joint EE. The whole EE process is fulfilled through a two-level hierarchical structure consisting of two policy networks for event detection and argument detection. The deep information interactions among the subtasks are realized, and it is more natural to deal with multiple events issue. Extensive experiments on ACE2005 and TAC2015 demonstrate the superiority of HPNet, leading to state-of-the-art performance and is more powerful for sentences with multiple events.

## introduction event extraction (ee) plays an important role in various real-life applications, such as information retrieval and news summarization @xcite . it aims to discover events with triggers and their corresponding arguments. typically, ee contains several subtasks: trigger identification, trigger classification, event argument identification, and argument role classification. some researchers handle these subtasks in a pipelined manner, i.e., perform trigger prediction and then identify arguments in separate stages, assuming that gold standard entities are provided @xcite @xcite . however, this staged manner has no strategy to take advantage of the deeper information interactions among these subtasks, so the upstream and the downstream subtasks cannot interfere with each other to improve their decisions. although there have been joint approaches which aim to build a joint extractor @xcite @xcite , they still follow a pipelined framework by first jointly predicting entities and triggers, and then scanning every entity-event pair for arguments and argument roles. one of the same limitations they share is that they produce redundant entity-event pairs information, thus bringing in possible errors. another is the mismatch between arguments and triggers when the sentence contains multiple events. for example, consider the following sentence: in baghdad, a cameraman died when an american tank fired on the palestine hotel. in this sentence, "cameraman" is not only a victim argument of event die (trigger "died"), but also a target argument of event attack (trigger "fired"). however, as "cameraman" is far away from the trigger "fired" in the sentence, the extractor may fail to identify "cameraman" as one argument for event attack. in view of this, we propose a hierarchical policy network (hpnet) to jointly solve the subtasks of ee, where 1) trigger identification and trigger classification are solved together, and 2) event argument identification and argument role classification are solved together by a hierarchy of an event-level policy network (pn) and an argument-level pn. this two-level hierarchical structure works as follows. during the scanning from the beginning to the end of a sentence, the event-level pn first detects the trigger and classifies its event type at each word. once a certain event is detected, an argument-level pn is triggered to detect participating arguments and classify the roles they play in the current event. when the argument-level process under this event is finished, the event-level pn continues its scan to search for other events in the sentence until it scans to the end. the learning of the ee subtasks is formulated as a sequential decision problem, which can be addressed by policy gradient method @xcite . hpnet realizes deep information interactions among the subtasks by passing state representations and rewards in the two-level hierarchical structure: the event-level pn passes fine-grained semantic information to the argument-level pn through state representations when triggering it, and the argumentlevel pn passes rewards back to the event-level pn to convey how well it is completed. besides, the event scheme information from the event-level decision also benefits the argument-level decision. as hpnet uses a hierarchical structure to detect the event first and then identify its participating arguments, redundant information and the mismatch issues can be potentially avoided. to sum up, our main contribution is at least three-fold: (1) to our knowledge, this is the first work applying policy network, a deep reinforcement learning method, to extract event. (2) we utilize a twolevel hierarchical structure to realize joint ee. with well-designed state representations, rewards, and an event scheme retrieval this paper proceeds as follows. first, we discuss the work that is related to pipelined ee, joint ee, and policy network ( §2). next, we present hpnet, a two-level policy network-based structure that contains a hierarchy of an event-level pn and an argument-level pn, followed with their hierarchical training details ( §3). then we describe the experiments on two benchmark datasets ace2005 and tac2015, followed by the results and discussions ( §4). we finish with a conclusion of the paper ( §5). ## related work pipelined event extraction the early work on ee has focused on the pipelined approach, which performs the subtasks of ee in separate stages @xcite @xcite . they either rely on manually designed features or use convolutional neural networks (cnns) to construct completely separate classifiers for trigger labeling and argument role classification, regarding entities as being provided by external annotators. recently, some methods extract entities and events to utilize the dependency of these two subtasks. above work exploits different structured prediction methods, including markov logic networks @xcite , dual decomposition @xcite , structured perceptron @xcite and attention-based graph cnn @xcite . some other methods develop inference models for events and argument roles, including structured predictions with markov logic @xcite and parameter sharing @xcite . however, the above studies are limited to modeling only two subtasks, either assuming the golden annotations for other subtasks or simply ignoring them. ## conclusion this paper presents a novel model hpnet, which approaches event extraction via a hierarchical policy network. in hpnet, the extraction process follows a two-level hierarchical structure: an event-level pn for events and an argument-level pn for participating arguments. thanks to the hierarchical structure, hpnet is good at modeling deep information interactions among the subtasks, and particularly better in dealing with the multiple events issue. comprehensive experiments demonstrate that our hpnet outperforms the state-of-the-art methods. as future work, this hpnet will be applied in cross-event or cross-document ee.