Kamel Nebhi


2025

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End-to-End Automated Item Generation and Scoring for Adaptive English Writing Assessment with Large Language Models
Kamel Nebhi | Amrita Panesar | Hans Bantilan
Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)

Automated item generation (AIG) is a key enabler for scaling language proficiency assessments. We present an end-to-end methodology for automated generation, annotation, and integration of adaptive writing items for the EF Standard English Test (EFSET), leveraging recent advances in large language models (LLMs). Our pipeline uses few-shot prompting with state-of-the-art LLMs to generate diverse, proficiency-aligned prompts, rigorously validated by expert reviewers. For robust scoring, we construct a synthetic response dataset via majority-vote LLM annotation and fine-tune a LLaMA 3.1 (8B) model. For each writing item, a range of proficiency-aligned synthetic responses, designed to emulate authentic student work, are produced for model training and evaluation. These results demonstrate substantial gains in scalability and validity, offering a replicable framework for next-generation adaptive language testing.

2023

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Automatic Assessment Of Spoken English Proficiency Based on Multimodal and Multitask Transformers
Kamel Nebhi | György Szaszák
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

This paper describes technology developed to automatically grade students on their English spontaneous spoken language proficiency with common european framework of reference for languages (CEFR) level. Our automated assessment system contains two tasks: elicited imitation and spontaneous speech assessment. Spontaneous speech assessment is a challenging task that requires evaluating various aspects of speech quality, content, and coherence. In this paper, we propose a multimodal and multitask transformer model that leverages both audio and text features to perform three tasks: scoring, coherence modeling, and prompt relevancy scoring. Our model uses a fusion of multiple features and multiple modality attention to capture the interactions between audio and text modalities and learn from different sources of information.

2012

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Ontology-Based Information Extraction from Twitter
Kamel Nebhi
Proceedings of the Workshop on Information Extraction and Entity Analytics on Social Media Data

2010

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FipsColor : grammaire en couleur interactive pour l’apprentissage du français
Jean-Philippe Goldman | Kamel Nebhi | Christopher Laenzlinger
Actes de la 17e conférence sur le Traitement Automatique des Langues Naturelles. Démonstrations

L’analyseur multilingue FiPS permet de transformer une phrase en une structure syntaxique riche et accompagnée d’informations lexicales, grammaticales et thématiques. On décrit ici une application qui adapte les structures en constituants de l’analyseur FiPS à une nomenclature grammaticale permettant la représentation en couleur. Cette application interactive et disponible en ligne (http://latl.unige.ch/fipscolor) peut être utilisée librement par les enseignants et élèves de primaire.