ICDM 2020 Knowledge Graph Contest: Consumer Event-Cause Extraction

TL;DR

This paper presents a novel sequence tagging framework for extracting consumer events and their causes from text, outperforming baselines and achieving top ranks in the ICDM 2020 contest.

Contribution

It introduces an end-to-end sequence tagging approach for simultaneous extraction of multiple event types and causes, advancing the state-of-the-art in event-cause extraction.

Findings

Our framework outperforms baseline methods.

Pre-trained BERT enhances extraction performance.

Achieved 1st and 3rd places in the competition stages.

Abstract

Consumer Event-Cause Extraction, the task aimed at extracting the potential causes behind certain events in the text, has gained much attention in recent years due to its wide applications. The ICDM 2020 conference sets up an evaluation competition that aims to extract events and the causes of the extracted events with a specified subject (a brand or product). In this task, we mainly focus on how to construct an end-to-end model, and extract multiple event types and event-causes simultaneously. To this end, we introduce a fresh perspective to revisit the relational event-cause extraction task and propose a novel sequence tagging framework, instead of extracting event types and events-causes separately. Experiments show our framework outperforms baseline methods even when its encoder module uses an initialized pre-trained BERT encoder, showing the power of the new tagging framework. In this competition, our team achieved 1st place in the first stage leaderboard, and 3rd place in the final stage leaderboard.