Papers with XGBoost
JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models (D19-50)
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| Challenge: | Detecting fake news is not well established yet, but it can be classified under several labels: false, biased, or framed to mislead the readers. |
| Approach: | They propose a deep learning model using BiLSTM, XGBoost, and BERT to detect propaganda using a corpus from a challenge. |
| Outcome: | The proposed model outperforms the baseline model on a dataset from the challenge NLP4IF 2019 . |
Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification (D19-1)
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| Challenge: | Feature importance is commonly used to explain machine predictions . however, the consistency of feature importance via different methods remains understudied . |
| Approach: | They compare feature importance from built-in mechanisms and post-hoc methods that approximate model behavior to find similarities between models. |
| Outcome: | The proposed methods show that features from traditional models are more similar with each other than with deep learning models. |
Enhancing Job Evaluation with Data Augmentation and Text Classification (2026.acl-industry)
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| Challenge: | Recruiters rely on job titles, role descriptions, and responsibility levels to determine job grades and salary structures. |
| Approach: | They propose to semi-automate job evaluation by fine-tuning a RoBERTa model for classification and using Gemini to generate synthetic job descriptions for rare job titles. |
| Outcome: | The proposed method improves job evaluation by boosting consistency and speeding up workflows. |
From Numbers to Narratives: Efficient Language Model-Based Detection for Safety-Critical Minority Classes (2026.findings-eacl)
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| Challenge: | Tabular datasets with high overall accuracy and poor performance on minority classes are often misaligned . a numbers to narratives framework improves overall accuracy by up to 22.43% in five of six datasets while maintaining computational feasibility. |
| Approach: | They propose a number-to-narrative framework that transforms tabular data into contextually rich descriptions. |
| Outcome: | The proposed framework achieves superior minority class F1-scores in five of six datasets. |
LBC: Language-Based-Classifier for Out-Of-Variable Generalization (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have great success in natural language processing tasks such as response generation, but their performance on tabular data tasks has been limited due to their inferior performance compared to traditional machine learning models (TMLs). |
| Approach: | They propose a Language-Based-Classifier (LBC) that maximizes the benefits of LLMs to outperform TMLs on OOV tasks. |
| Outcome: | The proposed model outperforms TMLs on OOV tasks by using three key methods. |