E vent G raph: Event Extraction as Semantic Graph Parsing (2022)

Abstract: Event extraction involves the detection and extraction of both the event triggers and the corresponding arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this paper, we propose EventGraph, a joint framework for event extraction, which encodes events as graphs. We represent event triggers and arguments as nodes in a semantic graph. Event extraction therefore becomes a graph parsing problem, which provides the following advantages: 1) performing event detection and argument extraction jointly; 2) detecting and extracting multiple events from a piece of text; 3) capturing the complicated interaction between event arguments and triggers. Experimental results on ACE2005 show that our model is competitive to state-of-the-art systems and has substantially improved the results on argument extraction. Additionally, we create two new datasets from ACE2005 where we keep the entire text spans for event arguments, instead of just the head word(s). Our code and models will be released as open-source.

## introduction event extraction aims at extracting event-related information from unstructured texts into structured form (i.e. triggers and arguments), according to a predefined event ontology @xcite . in these types of ontologies, events are characterized by event triggers, and comprise a set of predefined argument types. figure 1 shows an example of a sentence containing two events, an attack event triggered by "friendly-fire" and a die event triggered by "died"; the two events share the same arguments, but each plays a different role in the specific event. for instance, "u.s." is the agent in the die event, but plays the role of attacker in the attack event. ## related work our work is closely related to two research directions, event extraction and semantic parsing. supervised event extraction is an established research area in nlp. there are different methods to obtain the structured information of an event, and the mainstream methods can be divided into: 1) classification-based methods: treat event extraction as several classification subtasks, and either solve them separately in a pipeline-based manner @xcite @xcite @xcite or jointly infer multiple subtasks @xcite @xcite @xcite ; 2) generation-based approaches: formulate event extraction as a sequence generation problem @xcite @xcite ; 3) prompt tuning methods: inspired by natural language understanding tasks, these approaches take advantage of "discrete prompts" @xcite @xcite . meaning representation parsing has seen significant interest in recent years @xcite @xcite . unlike syntactic dependency representations, these semantic representations are crucially not trees, but rather general graphs, characterised by potentially having multiple entry points (roots) and not necessarily being connected, since not every token is a node in the graph. there has further been considerable progress in developing variants of both transition-based and graph-based dependency parsers capable of producing such semantic graphs @xcite @xcite . a recent and highly relevant development in the current context has been the application of semantic parsers to nlp tasks beyond meaning representation parsing. these approaches rely on the reformulation of task-specific representations to semantic dependency graphs. for example, @xcite exploit the parser of @xcite to predict spans of named entities, while @xcite phrase the task of negation resolution @xcite as a graph parsing task with promising results. recently, @xcite proposed a dependency parsing approach to extract opinion tuples from text, dubbed structured sentiment analysis, and a recent shared task dedicated to this task demonstrated the usefulness of graph parsing approaches to sentiment analysis @xcite . most similar to our work is the work by @xcite which adapts the perin parser @xcite to parse directly from raw text into sentiment graphs. ## event graph representations we adopt an efficient "labeled-edge" representation for event graph encoding within the scope of a sentence. each node in an event graph corresponds to either an event trigger or an argument, which is anchored to a unique text span in a sentence, except for the top node, which is only a dummy node for every event graph. the edges are constrained only between the top node and an event trigger, or between an event trigger and an argument, with the corresponding edge label as an event type or argument role. the "labeled-edge" encoding has the ability to represent: 1) multiple event mentions; 2) nested structures (overlapping between arguments or trigger-argument); 3) multiple argument roles of a single argument. taking the event graph from figure 2 as example, the sentence contains two event mentions, which share the same argument "the hills above chamchamal" but as different roles, and the argument "coalition" is nested inside the argument "coalition fighter jets". ## event parsing eventgraph is an adaptation of perin @xcite , a general permutation-invariant framework for text-to-graph parsing. given the "labeled-edge" encoding for event graphs, we create eventgraph by customizing the modules of perin as illustrated in figure 3 , which contains three classifiers to generate nodes, anchors, and edges, respectively. each input sequence is processed by four modules of eventgraph to generate a final structured representation. they moved away finetuned xlm-r q 1,1 q 1,2 q 2,1 q 2,2 q 3,1 q 3,2 transformer layers linear node @xmath0: eventgraph architecture. 1) the input gets a contextualized representation, 2) queries are generated for every input token, 3) queries are further processed with a decoder to predict 4a) node presence, 4b) node anchors, and 4c) edge labels. encoder we use the large version of xlm-r @xcite as the encoder to obtain contextualized representations of the input sequence; each token gets a contextual embedding via a learned subword attention layer over the subwords. ## results and discussion results show that eventgraph performs well on joint modelling of event triggers and arguments, and benefits from longer text spans for event triggers and arguments. when the full text spans of arguments are used, the model receives more training signals, so it has more information in differentiating sentences containing events from those without, as shown in to further look into our model's performance on identifying multi-token event arguments, especially those containing more than 5 tokens, we further report arg-c scores based on token-level span overlap. as shown in ## conclusion in this paper, we have proposed a new method for event extraction as semantic graph parsing. our proposed eventgraph has achieved competitive results on ace2005 for the task of event trigger classification, and obtained new state-of-the-art results for the task of argument role classification. we also provide a graph representation for better visualizing event mentions, and offer an efficient tool to facilitate graph conversion. we create two new datasets from ace2005, with the full text spans for both triggers and arguments, and offer the corresponding benchmark results. we show that despite adding more and longer text sequences, event-graph outperforms previous models tested on more restricted datasets. for future work, we would like to experiment with different pretrained language models, and carry out more detailed error analysis. our codes and models are released as open-source.