# Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification

arXiv: https://arxiv.org/abs/2203.02912 · HTML: https://mlsanity.com/paper/2203.02912/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 6 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Domain Adaptation with Adversarial Training and Graph Embeddings](https://mlsanity.com/paper/1805.05151/) (2018)
- [I-AID: Identifying Actionable Information from Disaster-related Tweets](https://mlsanity.com/paper/2008.13544/) (2020)
- [Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach](https://mlsanity.com/paper/1904.10876/) (2019)
- [Transformer-based Multi-task Learning for Disaster Tweet Categorisation](https://mlsanity.com/paper/2110.08010/) (2021)
- [Identifying Relevant Messages in a Twitter-based Citizen Channel for Natural Disaster Situations](https://mlsanity.com/paper/1503.05784/) (2015)
- [Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management](https://mlsanity.com/paper/2104.01436/) (2021)

## Built on by (most cited first, 5 of 5)

- [DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios](https://mlsanity.com/paper/2306.17271/) (2023)
- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations](https://mlsanity.com/paper/2402.01786/) (2024)
- [Foundation Models for Autonomous Robots in Unstructured Environments](https://mlsanity.com/paper/2407.14296/) (2024)
- [BanglaMM-Disaster: A Multimodal Transformer-Based Deep Learning Framework for Multiclass Disaster Classification in Bangla](https://mlsanity.com/paper/2511.21364/) (2025)

## Abstract (arXiv)

Online social media works as a source of various valuable and actionable information during disasters. These information might be available in multiple languages due to the nature of user generated content. An effective system to automatically identify and categorize these actionable information should be capable to handle multiple languages and under limited supervision. However, existing works mostly focus on English language only with the assumption that sufficient labeled data is available. To overcome these challenges, we propose a multilingual disaster related text classification system which is capable to work under mono, cross and multi lingual scenarios and under limited supervision. Our end-to-end trainable framework combines the versatility of graph neural networks, by applying over the corpus, with the power of transformer based large language models, over examples, with the help of cross-attention between the two. We evaluate our framework over total nine English, Non-English and monolingual datasets in mono, cross and multi lingual classification scenarios. Our framework outperforms state-of-the-art models in disaster domain and multilingual BERT baseline in terms of Weighted F1 score. We also show the generalizability of the proposed model under limited supervision.