PESE : Event Structure Extraction using Pointer Network based Encoder-Decoder Architecture (2022)

Abstract: The task of event extraction (EE) aims to find the events and event-related argument information from the text and represent them in a structured format. Most previous works try to solve the problem by separately identifying multiple substructures and aggregating them to get the complete event structure. The problem with the methods is that it fails to identify all the interdependencies among the event participants (event-triggers, arguments, and roles). In this paper, we represent each event record in a unique tuple format that contains trigger phrase, trigger type, argument phrase, and corresponding role information. Our proposed pointer network-based encoder-decoder model generates an event tuple in each time step by exploiting the interactions among event participants and presenting a truly end-to-end solution to the EE task. We evaluate our model on the ACE2005 dataset, and experimental results demonstrate the effectiveness of our model by achieving competitive performance compared to the state-of-the-art methods.

## introduction event extraction (ee) from text documents is one of the crucial tasks in natural language processing and understanding. event extraction deals with the identification of event-frames from natural language text. these event-frames have a complex structure with information regarding event-trigger, event type, event-specific arguments, and eventargument roles. for example, in baghdad, a cameraman died when an american tank fired on the palestine hotel. in this sentence died and fired are the event triggers for the event types die and attack respectively. the sentence contains entities phrases: baghdad, a cameraman, an american tank and palestine hotel. some of these entities play a specific role in these mentioned events and termed as event arguments. for event type die, (argument; role) pairs are: (baghdad; place), (a cameraman; victim), (american tank; instrument). whereas, for attack event, (argument; role) pairs are: (baghdad; place), (a cameraman; target), (american tank; instrument), and (palestine hotel; target). apparently, a sentence may contain multiple events; an entity may be shared by multiple event frames; moreover, a specific argument may play different roles in different event frames. therefore an ideal event extraction system will identify all the trigger words, classify the correct event types, extract all the eventspecific arguments and correctly predict the eventargument roles. each of these subtasks is equally important and challenging. most existing works decompose the ee task into these predefined subtasks and later aggregate those outputs to get the complete event frames. some of these models follow a pipelined approach where triggers and corresponding arguments are identified in separate stages. in contrast, others rely on joint modeling that predicts triggers and relevant arguments simultaneously. however, the pipeline approaches have to deal with error propagation problems, and the joint models have to exploit the information sharing and inter-dependency among the event triggers, arguments, and corresponding roles. the interaction among the event participants are of the following types: 1) inter-event interaction: usually event types in one sentence are interdependent of one another @xcite 2) intraevent argument interaction: arguments of a specific event-mention have some relationship among themselves @xcite @xcite 3) inter-event argument interaction: target entities or arguments shared by two different event mention present in a sentence generally have some inter-dependencies @xcite 4) event type-role interaction: each event frame has a distinct set of ar-gument roles based on its schema definition; hence event type and argument roles have an assiduous relationship. @xcite 5) argument-role interaction: the event-argument role is dependent on the entity types of the candidate arguments @xcite as well. significant efforts have been devoted to exploiting these interactions but despite their promising results, most of these existing systems failed to capture all these inter-dependencies @xcite . in order to exploit the interactions among the event participants mentioned above, we propose a neural network-based sequence to structure learning model that can generate sentence-level event frames from the input sentences. each event frame holds a (trigger, argument) phrase pair along with corresponding trigger type(event type) and rolelabel information. inspired from the models used for joint entity-relation extraction @xcite , aspect sentiment triplet extraction @xcite and semantic role labeling @xcite , we design a pointer network-based event structure extraction (pese) framework foot_0 that utilizes the event-argument-role interdependencies to extract the event frames from text. the encoder encodes the input sentence, whereas the decoder identifies an event frame in each time step based on the input sentence encoding and the event frames generated in the previous time steps. the innovation lies in the effectiveness of this type of modeling: 1) instead of decomposing the whole task into separate subtasks, our model can detect the trigger, argument, and role labels together 2)the system is capable of extracting multiple events present in a single sentence by generating each event-tuple in consecutive time steps, 3) the model is also able to extract multiple eventtuples with common trigger or argument phrases and 4)experimental results show that the model can identify the overlapping argument phrases present in the sentence as well. in summary, the contributions of this paper are: ## event frame representation given a sentence, our proposed end-to-end ee model extracts all the event-frames present in that sentence. these event frames are the structured representation of the event-specific information: (1) event trigger phrase, (2) event type, (3) argument phrase, (4) role label. inside the sentences, each trigger and argument phrase appears as a continuous sequence of words; hence, an effective way to represent these phrases is by their corresponding start and end locations. therefore in this paper, we represent each event-frame using a 6-tuple structure that stores all the records, as mentioned earlier. the 6-tuple contains: 1) start index of trigger phrase, 2) end index of trigger phrase, 3) event type, 4) start index of argument phrase, 5) end index of argument phrase 6) trigger-argument role label. the start and end index of the trigger phrase(1-2) denotes the event-trigger span, whereas the start and end index of argument phrase(4-5) represent the event-argument span and the other two records(3 and 6) are two labels: event type and role type. ## our proposed ee framework we employ a encoder-decoder architecture for the end-to-end ee task. the overview of the model architecture is depicted in figure 1 . the input to our model is a sentence (i.e. a sequence of tokens) and as output, we get a list of event tuples present in that sentence. we use pre-trained bert @xcite at the encoder and lstm (hochreiter and schmidhuber, 1997)-based network at the decoder in our model. ## related works based on the ace2005 guidelines the task of ee is the composition of three to four subtasks corresponding to different aspects of the event definition (nguyen and nguyen, 2019). a large number of prior works on ee only focus on some specific subtasks like: event detection (nguyen and grishman, 2015) @xcite or argument extraction @xcite ) @xcite ) @xcite . the models that are capable of extracting the complete event structure are categorized in mainly two ways: (1) pipelined-approach @xcite ) @xcite ) @xcite ) @xcite and (2) joint modeling approach @xcite @xcite ) @xcite ) @xcite . recently, methods like questionanswering (du and cardie, 2020) @xcite , machine reading comprehension @xcite , zero shot learning @xcite are also used to solve the ee problem. some of the recent works that follow sequence generation approach for event extraction also achieve promising results @xcite . among the previous methods the closest to our approach is text2event @xcite that also generates the event structure from sentences in end-to-end manner. but they generates the event representations in token by token format that means in each time step the model generates one single token. whereas our model generates one single event frame per time step which is more realistic in end-to-end event structure extraction. ## conclusion in this paper, we present a joint event extraction model that captures the event frames from text, exploiting intra-event and inter-event interactions in an end-to-end manner. unlike other methods that consider ee as a token classification problem or sequence labeling problem, we propose a sequenceto-tuple generation model that extracts an eventtuple containing trigger, argument, and role information in each time step. the experimental results indicate the effectiveness of our proposed approach. in the future, we plan to use cross-sentence context in our model and infuse event ontology information to improve our performance.