[Last updated on September 05th, 2022]

Research interests

My main area of research is Artificial Intelligence for Natural Language Processing, specifically deep learning models for natural language understanding and natural language generation.

Research topics of interest include:

• Deep Learning Theory
• Large Language Models
• Applications of NLP
• Machine Learning
• Recommender systems

Publications

• [HTML] Alexandros Xenos, John Pavlopoulos, Ion Androutsopoulos, Lucas Dixon, Jeffrey Sorensen and Leo Laugier: Toxicity Detection can be Sensitive to the Conversational Context. First Monday, 27(5) (September 2022)
• [PDF] John Pavlopoulos, Leo Laugier, Alexandros Xenos, Jeffrey Sorensen and Ion Androutsopoulos: From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer. ACL 2022
• [PDF] John Pavlopoulos, Jeffrey Sorensen, Leo Laugier and Ion Androutsopoulos: Semeval-2021 task 5: Toxic spans detection. SemEval-2021
• [PDF] Leo Laugier, John Pavlopoulos, Jeffrey Sorensen and Lucas Dixon: Civil rephrases of toxic texts with self-supervised transformers. EACL 2021
• [PDF] Leo Laugier*, Anran Wang*, Chuan-Sheng Foo, Theo Guenais and Vijay Chandrasekhar: Encoding Knowledge Graph with Graph CNN for Question Answering. Representation Learning on Graphs and Manifolds ICLR 2019 Workshop
• [arXiv] Leo Laugier, Daniil Bash, Jose Recatala, Hong Kuan Ng, Savitha Ramasamy, Chuan-Sheng Foo, Vijay R. Chandrasekhar, Kedar Hippalgaonkar: Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials. Machine Learning for Molecules and Materials NeurIPS 2018 Workshop