Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management

TL;DR

This paper introduces a novel graph neural network approach for unsupervised domain adaptation in disaster-related social media classification, effectively utilizing limited labeled data and outperforming existing methods.

Contribution

The paper proposes a two-part graph neural network that captures global and local information, improving disaster social media post classification under limited labeled data conditions.

Findings

Outperforms state-of-the-art methods by 2.74% in weighted F1 score.

Achieves 3.00% higher weighted F1 than BERT on granular datasets.

Retains performance with very limited labeled data.

Abstract

Identification and categorization of social media posts generated during disasters are crucial to reduce the sufferings of the affected people. However, lack of labeled data is a significant bottleneck in learning an effective categorization system for a disaster. This motivates us to study the problem as unsupervised domain adaptation (UDA) between a previous disaster with labeled data (source) and a current disaster (target). However, if the amount of labeled data available is limited, it restricts the learning capabilities of the model. To handle this challenge, we utilize limited labeled data along with abundantly available unlabeled data, generated during a source disaster to propose a novel two-part graph neural network. The first-part extracts domain-agnostic global information by constructing a token level graph across domains and the second-part preserves local instance-level semantics. In our experiments, we show that the proposed method outperforms state-of-the-art techniques by $2.74\%$ weighted F$_1$ score on average on two standard public dataset in the area of disaster management. We also report experimental results for granular actionable multi-label classification datasets in disaster domain for the first time, on which we outperform BERT by $3.00\%$ on average w.r.t weighted F$_1$. Additionally, we show that our approach can retain performance when very limited labeled data is available.