14 papers
Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations
Galo Castillo-López, Alexis Lombard, Gaël de Chalendar +1
We propose a multi-task learning approach for multi-party dialogue intent recognition that leverages an auxiliary task that models turn-taking dynamics. Specifically, we introduce…
AraSSM: A bidirectional state-space encoder for Arabic masked language modeling
Ahmed Amine Aliane, Hassina Aliane, Nasredine Semmar
Pretrained Transformer encoders such as AraBERT, MARBERT, and CAMeLBERT have become the standard backbone for Arabic natural language understanding, but their self-attention mechan…
Decomposed Entailment for Factuality Checking and Hallucination Detection
Achir Oukelmoun, Nasredine Semmar, Gaël De Chalendar
The reliability of Large Language Models (LLMs) is often compromised by factual inconsistencies, including hallucinations---cases where generated content is not supported by the un…
Efficient Multilingual Neural Machine Translation via Corpus-Driven Vocabulary Pruning: An English-Arabic Case Study
Ahmed Amine Aliane, Nasredine Semmar, Hassina Aliane
The adoption of large pre-trained multilingual models for neural machine translation (MNMT) faces a major challenge: excessive memory and computational consumption due to overly la…
Injecting Structured Biomedical Knowledge into Language Models: Continual Pretraining vs. GraphRAG
Jaafer Klila, Sondes Bannour Souihi, Rahma Boujelben +2
The injection of domain-specific knowledge is crucial for adapting language models (LMs) to specialized fields such as biomedicine. While most current approaches rely on unstructur…
How DDAIR you? Disambiguated Data Augmentation for Intent Recognition
Galo Castillo-López, Alexis Lombard, Nasredine Semmar +1
Large Language Models (LLMs) are effective for data augmentation in classification tasks like intent detection. In some cases, they inadvertently produce examples that are ambiguou…
MMSE-Calibrated Few-Shot Prompting for Alzheimer's Detection
Jana Sweidan, Mounim A. El-Yacoubi, Nasredine Semmar
Prompting large language models is a training-free method for detecting Alzheimer's disease from speech transcripts. Using the ADReSS dataset, we revisit zero-shot prompting and st…
Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations
Galo Castillo-López, Gaël de Chalendar, Nasredine Semmar
Intent recognition is a fundamental component in task-oriented dialogue systems (TODS). Determining user intents and detecting whether an intent is Out-of-Scope (OOS) is crucial fo…
From LIMA to DeepLIMA: following a new path of interoperability
Victor Bocharov, Romaric Besançon, Gaël de Chalendar +2
In this article, we describe the architecture of the LIMA (Libre Multilingual Analyzer) framework and its recent evolution with the addition of new text analysis modules based on d…
Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents
Andrew H. Lee, Sina J. Semnani, Galo Castillo-López +16
Creating multilingual task-oriented dialogue (TOD) agents is challenging due to the high cost of training data acquisition. Following the research trend of improving training data…
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
Mehrad Moradshahi, Tianhao Shen, Kalika Bali +16
Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost…
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units
Sara Meftah, Nasredine Semmar, Youssef Tamaazousti +2
Neural Transfer Learning (TL) is becoming ubiquitous in Natural Language Processing (NLP), thanks to its high performance on many tasks, especially in low-resourced scenarios. Nota…
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
Sara Meftah, Youssef Tamaazousti, Nasredine Semmar +2
Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems a…
Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks
Othman Zennaki, Nasredine Semmar, Laurent Besacier
This work focuses on the rapid development of linguistic annotation tools for resource-poor languages. We experiment several cross-lingual annotation projection methods using Recur…