An Effective System for Multi-format Information Extraction

TL;DR

This paper presents a comprehensive system for multi-format information extraction, converting various subtasks into suitable formats and integrating models for relation and event extraction, achieving competitive results in a challenge.

Contribution

It introduces novel conversion and multi-task learning techniques for relation and event extraction across multiple formats, advancing the state-of-the-art in multi-format IE systems.

Findings

Achieved top-4 ranking in the challenge

Relation extraction F1 score of 79.887%

Event extraction F1 scores of 85.179% (sentence-level) and 70.828% (document-level)

Abstract

The multi-format information extraction task in the 2021 Language and Intelligence Challenge is designed to comprehensively evaluate information extraction from different dimensions. It consists of an multiple slots relation extraction subtask and two event extraction subtasks that extract events from both sentence-level and document-level. Here we describe our system for this multi-format information extraction competition task. Specifically, for the relation extraction subtask, we convert it to a traditional triple extraction task and design a voting based method that makes full use of existing models. For the sentence-level event extraction subtask, we convert it to a NER task and use a pointer labeling based method for extraction. Furthermore, considering the annotated trigger information may be helpful for event extraction, we design an auxiliary trigger recognition model and use the multi-task learning mechanism to integrate the trigger features into the event extraction model. For the document-level event extraction subtask, we design an Encoder-Decoder based method and propose a Transformer-alike decoder. Finally,our system ranks No.4 on the test set leader-board of this multi-format information extraction task, and its F1 scores for the subtasks of relation extraction, event extractions of sentence-level and document-level are 79.887%, 85.179%, and 70.828% respectively. The codes of our model are available at {https://github.com/neukg/MultiIE}.