Papers by Sahar Ghannay
mALBERT: Is a Compact Multilingual BERT Model Still Worth It? (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies on the ethical and ecological impact of pre-trained language models raise questions about the temporal, financial, and environmental aspects of such models. |
| Approach: | They propose to focus on smaller models, such as compact models like ALBERT, which are more ecologically virtuous than these PLMs. |
| Outcome: | The proposed model is compared with classical multilingual models and is ethically virtuous. |
The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools (2022.lrec-1)
Copied to clipboard
Gaëlle Laperrière, Valentin Pelloin, Antoine Caubrière, Salima Mdhaffar, Nathalie Camelin, Sahar Ghannay, Bassam Jabaian, Yannick Estève
| Challenge: | a growing number of studies address the spoken language understanding domain through a simple task like speech intent detection. |
| Approach: | They focus on the french MEDIA SLU dataset, which is distributed since 2005 . they propose a recipe for its use, including data preparation, training and evaluation scripts . |
| Outcome: | The MEDIA SLU dataset is used as a benchmark dataset for a large number of research projects. |
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding (2022.lrec-1)
Copied to clipboard
Salima Mdhaffar, Valentin Pelloin, Antoine Caubrière, Gaëlle Laperriere, Sahar Ghannay, Bassam Jabaian, Nathalie Camelin, Yannick Estève
| Challenge: | Pretrained models have been introduced for both acoustic and language modeling. |
| Approach: | They present an error analysis of pretrained models using a french MEDIA benchmark dataset. |
| Outcome: | The proposed models have been able to improve on the french MEDIA benchmark dataset, which is one of the most challenging among all benchmarks accessible to the entire research community. |
Simulating ASR errors for training SLU systems (L18-1)
Copied to clipboard
| Challenge: | Existing methods to simulate automatic speech recognition errors from manual transcriptions are not available during training of the SLU model. |
| Approach: | They propose to use acoustic and linguistic word embeddings to define a similarity measure between words to predict ASR confusions. |
| Outcome: | The proposed method significantly improves the performance of spoken language understanding systems. |
Small Language Models Are Good Too: An Empirical Study of Zero-Shot Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Using small language models, we challenge the dominance of large models in text classification by prompting. |
| Approach: | They compare the performance of small and large language models in a zero-shot context using different architectures and scoring functions. |
| Outcome: | The proposed model outperforms large models in a zero-shot context. |
Neural Networks approaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation Task (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies have focused on English language and tasks, but few have explored the complexity of a SLU task. |
| Approach: | They propose to explore Neural Networks approaches for a French Spoken Language Understanding task. |
| Outcome: | The proposed approach outperforms classical Neural Network Architectures and achieves state-of-the-art results. |
New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark (2024.lrec-main)
Copied to clipboard
| Challenge: | Intent classification and slot-filling tasks are essential tasks of Spoken Language Understanding (SLU). |
| Approach: | They propose to use a MEDIA SLU dataset to train a multilingual model to achieve both tasks jointly. |
| Outcome: | The proposed model can be trained on multiple datasets including the MEDIA dataset and extends to more tasks and use cases. |
Multi-Level Information Retrieval Augmented Generation for Knowledge-based Visual Question Answering (2024.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge-Aware Visual Question Answering about Entity tasks require two separate steps to generate accurate answers. |
| Approach: | They propose a multi-level information RAG approach that enhances answer generation through entity retrieval and query expansion. |
| Outcome: | The proposed approach improves answer generation through entity retrieval and query expansion. |