Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022)

Abstract: Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols. These approaches are usually limited to a set of pre-defined types. We propose a novel event extraction framework that uses event types and argument roles as natural language queries to extract candidate triggers and arguments from the input text. With the rich semantics in the queries, our framework benefits from the attention mechanisms to better capture the semantic correlation between the event types or argument roles and the input text. Furthermore, the query-and-extract formulation allows our approach to leverage all available event annotations from various ontologies as a unified model. Experiments on ACE and ERE demonstrate that our approach achieves state-of-the-art performance on each dataset and significantly outperforms existing methods on zero-shot event extraction.

## introduction event extraction @xcite @xcite is a task to identify and type event triggers and participants from natural language text. as shown in figure 1 , married and left are triggers of two event mentions of the marry and transport event types respectively. two arguments are involved in the left event mention: she is an artifact, and irap is the destination. traditional studies usually model event extraction as a multi-class classification problem @xcite @xcite @xcite , where a set of event types are first defined, and then supervised machine learning approaches will detect and classify each candidate event mention or argument into one of the 1 our code is open sourced at https://github.com/ vt-nlp/event_query_extract for reproduction purpose. target types. however, each event type or argument role is treated as an atomic symbol, ignoring their rich semantics in these approaches. several studies explore the semantics of event types by leveraging the event type structures @xcite , seed event mentions @xcite , or question answering (qa) @xcite . however, these approaches are still designed for and thus limited to a single target event ontology foot_0 , such as ace @xcite or ere @xcite . with the existence of multiple ontologies and the challenge of handling new emerging event types, it is necessary to study event extraction approaches that are generalizable and can use all available training data from distinct event ontologies. 3 to this end, we propose a new event extraction framework following a query-and-extract paradigm. our framework represents event types and argument roles as natural language queries with rich semantics. the queries are then used to extract the corresponding event triggers and arguments by leveraging our proposed attention mechanism to capture their interactions with input texts. specifically, (1) for trigger detection, we formulate each event type as a query based on its type name and a short list of prototype triggers, and make binary decoding of each token based on its query-aware h1 trigger-aware entity embedding @xcite transport arrival travels expelled @xcite destination agent place origin artifact h3 @xcite ## our approach as figure 2 shows, given an input sentence, we first identify the candidate triggers for each event type by taking it as a query to the sentence. each event type, such as attack, is represented with a natural language text, including its type name and a shortlist of prototype triggers, such as invaded and airstrikes, which are selected from the training examples. then, we concatenate the input sentence with the event type query, encode them with a pre-trained bert encoder @xcite , compute the attention distribution over the sequential representation of the event type query for each input token, and finally classify each token into a binary label, indicating it as a trigger candidate of the specific event type or not. to extract the arguments for each candidate trigger, we follow a similar strategy and take the set of pre-defined argument roles for its corresponding event type as a query to the input sentence. we use another bert encoder to learn the contextual representations for the input sentence and the query of the argument roles. then, we take each entity of the input sentence as a candidate argument and compute the semantic correlation between entities and argument roles with multiway attention, and finally classify each entity into a binary label in terms of each argument role. ## related work traditional event extraction studies @xcite @xcite @xcite @xcite @xcite @xcite usually detect event triggers and arguments with multi-class classifiers. unlike all these methods that treat event types and argument roles as symbols, our approach considers them queries with rich semantics and leverages the semantic interaction between input tokens and each event type or argument role. several studies have explored the semantics of event types based on seed event triggers @xcite @xcite , event type structures @xcite , definitions @xcite and latent representations @xcite . however, they can hardly be generalized to argument extraction. question answering based event extraction @xcite @xcite can take advantage of the semantics of event types and the large-scale question answering datasets. compared with these methods, there are three different vital designs, making our approach perform and be generalized better than these qa-based approaches: (1) our approach directly takes event types and argument roles as queries. in contrast, previous qa-based approaches rely on templates or generative modules to create natural language questions. however, it is difficult to find the optimal format of questions for each event type, and many studies @xcite have shown that mrc or qa models are sensitive to the subtle change of the questions. (2) qa-based approaches can only detect arguments for one argument role at once, while our approach extracts all arguments of an event trigger with onetime encoding and decoding, which is more efficient and leverages the implicit relations among the candidate arguments or argument roles. (3) qa-based approaches rely on span prediction to extract arguments without requiring entity extraction, which could result in more entity boundary errors. thus we choose to pre-train a name tagging model and use binary decoding over system detected entities.moreover, it is pretty challenging to fully adapt the event extraction task to the spanbased question answering. the main reason is that each sentence may contain multiple triggers for a particular event type. even if we can formalize a question, e.g., "what is the trigger for attack?" it is difficult for the model to return all the spans of event triggers correctly. ## conclusion and future work we refine event extraction with a query-and-extract paradigm and design a new framework that leverages rich semantics of event types and argument roles and captures their interactions with input texts using attention mechanisms to extract event triggers and arguments. experimental results demonstrate that our approach achieves state-of-the-art performance on supervised event extraction and shows prominent accuracy and generalizability to new event types and across ontologies. in the future, we will explore better representations of event types and argument roles and combine them prompt tuning approach further to improve the accuracy and generalizability of event extraction. thien huu nguyen, kyunghyun cho, and ralph grishman. 2016. joint event extraction via recurrent neural networks. in proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: human language technologies, pages 300-309, san diego, california. association for computational linguistics. thien huu nguyen and ralph grishman. 2016. modeling skip-grams for event detection with convolutional neural networks. in proceedings of the 2016 conference on empirical methods in natural language processing, pages 886-891, austin, texas. association for computational linguistics. alexandre passos, vineet kumar, and andrew mccallum. 2014. lexicon infused phrase embeddings for named entity resolution. in proceedings of the eighteenth conference on computational natural language learning, pages 78-86, ann arbor, michigan. association for computational linguistics.