5 papers · 1 filter
LLM-based Atomic Propositions help weak extractors: Evaluation of a Propositioner for triplet extraction
Luc Pommeret, Thomas Gerald, Patrick Paroubek +3
Knowledge Graph construction from natural language requires extracting structured triplets from complex, information-dense sentences. In this paper, we investigate if the decomposi…
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…
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…
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…
On the Usability of Transformers-based models for a French Question-Answering task
Oralie Cattan, Christophe Servan, Sophie Rosset
For many tasks, state-of-the-art results have been achieved with Transformer-based architectures, resulting in a paradigmatic shift in practices from the use of task-specific archi…