Search Results for author:
Found 4 papers, 1 papers with code
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish
Pre-trained Language Models such as BERT have become ubiquitous in NLP where they have achieved state-of-the-art performance in most NLP tasks.
Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance
This paper revisits recent code similarity evaluation metrics, particularly focusing on the application of Abstract Syntax Tree (AST) editing distance in diverse programming languages.
Soft Prompt Tuning for Cross-Lingual Transfer: When Less is More
Soft Prompt Tuning (SPT) is a parameter-efficient method for adapting pre-trained language models (PLMs) to specific tasks by inserting learnable embeddings, or soft prompts, at the input layer of the PLM, without modifying its parameters.
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition
In this paper, we compare three transformer-based models (BERT, RoBERTa, and XLNet) to two non-transformer-based models (CRF and BiLSTM-CNN-CRF).
