PESE: Event Structure Extraction using Pointer Network based Encoder-Decoder Architecture
This addresses event extraction for natural language processing by offering an incremental improvement over existing methods.
The paper tackles event extraction by proposing an end-to-end pointer network-based encoder-decoder model that generates event tuples to capture interdependencies among event participants, achieving competitive performance on the ACE2005 dataset.
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.