Papers with formality classification

2 papers
Cross-lingual Text Classification Transfer: The Case of Ukrainian (2025.coling-main)

Copied to clipboard

Challenge: despite the large amount of labeled datasets, there is an imbalance in data availability across languages.
Approach: They explore cross-lingual knowledge transfer methods avoiding manual data curation . they use large multilingual encoders and translation systems, LLMs, and language adapters .
Outcome: The proposed approaches are tested on three text classification tasks in Ukrainian . the authors show that the proposed approaches avoid manual data curation .
Cross-Cultural Transfer Learning for Text Classification (D19-1)

Copied to clipboard

Challenge: a large dataset is required to achieve competitive performance in most natural language tasks. large datasets are expensive, time consuming, and error-prone.
Approach: They propose a transfer-learning framework that leverages bilingual corpora for natural language text classification using no task-specific data.
Outcome: The proposed framework can achieve good performance on formality classification and sarcasm detection tasks without any task-specific labeled data.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations