277 citations · 307 across the 12 of their papers we have counts for
14 papers
Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning
Pierre Lepagnol, Sahar Ghannay, Thomas Gerald +2
Understanding user queries is fundamental in many applications, such as home assistants, booking systems, or recommendations. Accordingly, it is crucial to develop accurate Spoken…
A dual task learning approach to fine-tune a multilingual semantic speech encoder for Spoken Language Understanding
Gaëlle Laperrière, Sahar Ghannay, Bassam Jabaian +1
Self-Supervised Learning is vastly used to efficiently represent speech for Spoken Language Understanding, gradually replacing conventional approaches. Meanwhile, textual SSL model…
Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification
Pierre Lepagnol, Thomas Gerald, Sahar Ghannay +2
This study is part of the debate on the efficiency of large versus small language models for text classification by prompting.We assess the performance of small language models in…
New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark
Nadège Alavoine, Gaëlle Laperriere, Christophe Servan +2
Intent classification and slot-filling are essential tasks of Spoken Language Understanding (SLU). In most SLUsystems, those tasks are realized by independent modules. For about fi…
mALBERT: Is a Compact Multilingual BERT Model Still Worth It?
Christophe Servan, Sahar Ghannay, Sophie Rosset
Within the current trend of Pretained Language Models (PLM), emerge more and more criticisms about the ethical andecological impact of such models. In this article, considering the…
Semantic enrichment towards efficient speech representations
Gaëlle Laperrière, Ha Nguyen, Sahar Ghannay +2
Over the past few years, self-supervised learned speech representations have emerged as fruitful replacements for conventional surface representations when solving Spoken Language…
Benchmarking Transformers-based models on French Spoken Language Understanding tasks
Oralie Cattan, Sahar Ghannay, Christophe Servan +1
In the last five years, the rise of the self-attentional Transformer-based architectures led to state-of-the-art performances over many natural language tasks. Although these appro…
Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation
Juan M. Coria, Hervé Bredin, Sahar Ghannay +1
We propose to address online speaker diarization as a combination of incremental clustering and local diarization applied to a rolling buffer updated every 500ms. Every single step…
Where are we in semantic concept extraction for Spoken Language Understanding?
Sahar Ghannay, Antoine Caubrière, Salima Mdhaffar +3
Spoken language understanding (SLU) topic has seen a lot of progress these last three years, with the emergence of end-to-end neural approaches. Spoken language understanding refer…
LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis
Somnath Banerjee, Sahar Ghannay, Sophie Rosset +2
This paper describes the participation of LIMSI UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text. The proposed approach competed in SentiMix Hin…
A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification
Juan M. Coria, Hervé Bredin, Sahar Ghannay +1
Despite the growing popularity of metric learning approaches, very little work has attempted to perform a fair comparison of these techniques for speaker verification. We try to fi…
End-to-end named entity extraction from speech
Sahar Ghannay, Antoine Caubrière, Yannick Estève +2
Named entity recognition (NER) is among SLU tasks that usually extract semantic information from textual documents. Until now, NER from speech is made through a pipeline process th…
TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation
François Hernandez, Vincent Nguyen, Sahar Ghannay +2
In this paper, we present TED-LIUM release 3 corpus dedicated to speech recognition in English, that multiplies by more than two the available data to train acoustic models in comp…
ASR error management for improving spoken language understanding
Edwin Simonnet, Sahar Ghannay, Nathalie Camelin +2
This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems. In this study, the S…