Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding (2020)

Abstract: Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is spread across multiple sentences. We argue that document-level event extraction is a difficult task since it requires a view of a larger context to determine which spans of text correspond to event role fillers. We first investigate how end-to-end neural sequence models (with pre-trained language model representations) perform on document-level role filler extraction, as well as how the length of context captured affects the models’ performance. To dynamically aggregate information captured by neural representations learned at different levels of granularity (e.g., the sentence- and paragraph-level), we propose a novel multi-granularity reader. We evaluate our models on the MUC-4 event extraction dataset, and show that our best system performs substantially better than prior work. We also report findings on the relationship between context length and neural model performance on the task.

## introduction the goal of document-level event extraction 1 is to identify in an article events of a pre-specified type along with their event-specific role fillers, i.e., arguments. the complete document-level extraction problem generally requires role filler extraction, noun phrase coreference resolution and event tracking (i.e., determine which extracted role fillers belong to which event). in this work, we focus only on document-level role filler extraction. figure 1 provides a representative example of this task. given an article consisting of multiple paragraphs/sentences, and a fixed set of event types 1 the task is also referred to as template filling @xcite . ## related work event extraction has been mainly studied under two paradigms: detecting the event trigger and extracting the arguments from an individual sentence (e.g., the ace task @xcite foot_0 , vs. at the document level (e.g., the muc-4 template-filling task @xcite ). ## methodology in the following we describe (1) how we transform the document into paired token-tag sequences and formalize the task as a sequence tagging problem (section 3.1); (2) the architectures of our base ksentence reader (section 3.2) and multi-granularity reader (section 3.3). ## experiments and analysis we evaluate our models' performance on the muc-4 event extraction benchmark @xcite , and compare to prior work. we also report findings on the effect of context length on the end-to-end readers' performance on this document-level task. ## conclusion and future work we have demonstrated that document-level event role filler extraction could be successfully tackled with end-to-end neural sequence models. investigations on how the input context length affects the neural sequence readers' performance show that context of very long length might be hard for the neural models to capture and results in lower performance. we propose a novel multi-granularity reader to dynamically incorporate paragraph-and sentence-level contextualized representations. evaluations on the benchmark dataset and qualitative analysis prove that our model achieves substantial improvement over prior work. in the future work, it would be interesting to further explore how the model can be adapted to jointly extract role fillers, tackles coreferential mentions and constructing event templates.