Semi-Supervised Granular Classification Framework for Resource Constrained Short-texts

Ghosh, Samujjwal; Desarkar, Maunendra Sankar

doi:10.1145/3394231.3397892

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…We combine the two embedding spaces with a novel end-toend scaled dot product cross attention mechanism which learns to attend on corpus level context information given an example, where the downstream task is text classification. In addition, we enable multilinguality in our model by making it applicable to realistic disaster situations while most existing works on disaster response domain are monolingual [1,9,12] only. In § 3.1, we discuss our method for obtaining example level contextual word embeddings with recent transformer based models (e.g BERT [8] or XLM-R [5]).…”

Section: Proposed Frameworkmentioning

confidence: 99%

“…However, these user-generated contents, such as tweets, are generally in languages native to the location of the disaster. On the other hand, majority of works in the literature focus mostly on English language [1,3,9,12,17,21,22] only. Understanding and processing texts in multiple languages is of paramount importance for effective disaster mitigation.…”

Section: Introductionmentioning

confidence: 99%

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Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification

Ghosh¹,

Maji²,

Desarkar³

2022

Preprint

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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 undervmonolingual, cross-lingual and multilingual 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 invmonolingual, cross-lingual and multilingual lingual classification scenarios. Our framework outperforms state-of-the-art models in disaster domain and multilingual BERT baseline in terms of Weighted F 1 score. We also show the generalizability of the proposed model under limited supervision.

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Section: Proposed Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification

Ghosh¹,

Maji²,

Desarkar³

2022

Preprint

Self Cite

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“…Although existing works utilize such information to build models for crisis event analysis, standard supervised approaches require annotating vast amounts of data during disasters, which is impractical due to limited response time (Li et al, 2015;Caragea et al, 2016;Li et al, 2017Neppalli et al, 2018;Ray Chowdhury et al, 2020;Sosea et al, 2021). On the other hand, current semi-supervised models can be biased, performing moderately well for certain classes while extremely worse for others, resulting in a detrimentally negative effect on disaster monitoring and analysis (Alam et al, 2018;Ghosh and Desarkar, 2020;Sirbu et al, 2022;Zou et al, 2023;Wang et al, 2023a). For instance, neglecting life-essential classes, such as requests or urgent needs, displaced people & evacuations and injured or dead people, can have severely adverse consequences for relief efforts.…”

Section: Introductionmentioning

confidence: 99%

DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank

Zou,

Zhou,

Zhang

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. Emergency relief organizations leverage such information to acquire timely crisis circumstances and expedite rescue operations. While existing works utilize such information to build models for crisis event analysis, fully-supervised approaches require annotating vast amounts of data and are impractical due to limited response time. On the other hand, semi-supervised models can be biased, performing moderately well for certain classes while performing extremely poorly for others, resulting in substantially negative effects on disaster monitoring and rescue. In this paper, we first study two recent debiasing methods on semi-supervised crisis tweet classification. Then we propose a simple but effective debiasing method, DeCrisisMB, that utilizes a Memory Bank to store and perform equal sampling for generated pseudo-labels from each class at each training iteration. Extensive experiments are conducted to compare different debiasing methods' performance and generalization ability in both in-distribution and outof-distribution settings. The results demonstrate the superior performance of our proposed method. Our code is available at https: //github.com/HenryPengZou/DeCrisisMB.

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“…One primary reason for this is the difficulty in obtaining a sufficiently large number of annotations on time to assist development of a classifier. The problem has been explored in literature [1], [4] with the assumption that sufficient amount of labeled data is available. However, this assumption might not be realistic during a disaster.…”

Section: Introductionmentioning

confidence: 99%

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

Ghosh,

Maji,

Desarkar

2021

Preprint

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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 F1 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 F1. Additionally, we show that our approach can retain performance when very limited labeled data is available. We plan to release our code publicly.

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Semi-Supervised Granular Classification Framework for Resource Constrained Short-texts

Cited by 7 publications

References 7 publications

Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification

Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification

DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank

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

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