Papers with adaptive pre-training

2 papers
KALA: Knowledge-Augmented Language Model Adaptation (2022.naacl-main)

Copied to clipboard

Challenge: Pre-trained language models (PLMs) have proved to be effective on various natural language understanding tasks.
Approach: They propose a domain adaption framework which modulates the intermediate hidden representations of PLMs with domain knowledge, consisting of entities and their relational facts.
Outcome: The proposed framework outperforms adaptive pre-training on question answering and named entity recognition tasks on multiple datasets across different domains.
Chinese Automatic Readability Assessment Using Adaptive Pre-training and Linguistic Feature Fusion (2025.coling-main)

Copied to clipboard

Challenge: Existing methods for classification of reading difficulty of texts are insufficiently trained and lack of linguistic features.
Approach: They propose a method that combines adaptive pre-training with feature fusion to capture different text difficulties and an interactive attention mechanism to integrate linguistic and deep features.
Outcome: The proposed method achieves state-of-the-art (SOTA) performance on Chinese textbook dataset and can be applied to other languages.

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