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Cross-Lingual Document Classification
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Cross-lingual document classification refers to the task of using data and models available for one language for which ample such resources are available (e.g., English) to solve classification tasks in another, commonly low-resource, language.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change
Synthetic News Generation for Fake News Classification
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data
A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit
A Self-Learning Multimodal Approach for Fake News Detection
LLM Teacher-Student Framework for Text Classification With No Manually Annotated Data: A Case Study in IPTC News Topic Classification
On Limitations of LLM as Annotator for Low Resource Languages
BERT or FastText? A Comparative Analysis of Contextual as well as Non-Contextual Embeddings
CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM
EthioMT: Parallel Corpus for Low-resource Ethiopian Languages
Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language Models
Improving Black-box Robustness with In-Context Rewriting
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble
Intrinsic User-Centric Interpretability through Global Mixture of Experts
LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization
The effect of stemming and lemmatization on Portuguese fake news text classification
Benchmarking Multilabel Topic Classification in the Kyrgyz Language
Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models
A Dataset and Strong Baselines for Classification of Czech News Texts
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text
MasakhaNEWS: News Topic Classification for African languages
Analyzing the Generalizability of Deep Contextualized Language Representations For Text Classification
Classifying the Ideological Orientation of User-Submitted Texts in Social Media
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Multiverse: Multilingual Evidence for Fake News Detection
Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language
Potrika: Raw and Balanced Newspaper Datasets in the Bangla Language with Eight Topics and Five Attributes
BaIT: Barometer for Information Trustworthiness
Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model
Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification
MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection
The use of Data Augmentation as a technique for improving neural network accuracy in detecting fake news about COVID-19
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification
Does Transliteration Help Multilingual Language Modeling?
Automated Fake News Detection using cross-checking with reliable sources
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