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Jacques Klein

7 accepted papers

2025

LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings

COLING 2025main

Sentence embedding models play a key role in various Natural Language Processing tasks, such as in Topic Modeling, Document Clustering and Recommendation Systems. However, these models rely heavily on parallel data, which can be scarce for many low-resource languages, including Luxembourgish. This s…

2025

SynFix: Dependency-Aware Program Repair via RelationGraph Analysis

ACL 2025finding

Recent advancements in large language models (LLMs) have significantly improved software development automation, including bug localization, code synthesis, program repair, and test generation. However, most prior work on program repair focuses on isolated elements, such as classes or functions, neg…

2024

CodeAgent: Autonomous Communicative Agents for Code Review

EMNLP 2024main

Code review, which aims at ensuring the overall quality and reliability of software, is a cornerstone of software development. Unfortunately, while crucial, Code review is a labor-intensive process that the research community is looking to automate. Existing automated methods rely on single input-ou…

2024

Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance

ACL 2024short

This paper revisits recent code similarity evaluation metrics, particularly focusing on the application of Abstract Syntax Tree (AST) editing distance in diverse programming languages. In particular, we explore the usefulness of these metrics and compare them to traditional sequence similarity metri…

2023

Evaluating Parameter-Efficient Finetuning Approaches for Pre-trained Models on the Financial Domain

EMNLP 2023short findings

Large-scale language models with millions, billions, or trillions of trainable parameters are becoming increasingly popular. However, they risk becoming rapidly over-parameterized and the adaptation cost of fully fine-tuning them increases significantly. Storing them becomes progressively impractica…

Cited by 0SourceScholar
2023

MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning

AAAI 2023technical

Representation learning of source code is essential for applying machine learning to software engineering tasks. Learning code representation from a multilingual source code dataset has been shown to be more effective than learning from single-language datasets separately, since more training data f…

2020

Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition

COLING 2020main

Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task and has remained an active research field. In recent years, transformer models and more specifically the BERT model developed at Google revolutionised the field of NLP. While the performance of transformer-based a…