# A Spoken Drug Prescription Dataset in French for Spoken Language Understanding

arXiv: https://arxiv.org/abs/2207.08292 · HTML: https://mlsanity.com/paper/2207.08292/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [A Survey of Available Corpora for Building Data-Driven Dialogue Systems](https://mlsanity.com/paper/1512.05742/) (2015)
- [Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning](https://mlsanity.com/paper/1606.02560/) (2016)
- [Neural Medication Extraction: A Comparison of Recent Models in Supervised and Semi-supervised Learning Settings](https://mlsanity.com/paper/2110.10213/) (2021)

## Built on by (most cited first, 1 of 1)

- [Spoken Dialogue System for Medical Prescription Acquisition on Smartphone: Development, Corpus and Evaluation](https://mlsanity.com/paper/2311.03510/) (2023)

## Abstract (arXiv)

Spoken medical dialogue systems are increasingly attracting interest to enhance access to healthcare services and improve quality and traceability of patient care. In this paper, we focus on medical drug prescriptions acquired on smartphones through spoken dialogue. Such systems would facilitate the traceability of care and would free clinicians' time. However, there is a lack of speech corpora to develop such systems since most of the related corpora are in text form and in English. To facilitate the research and development of spoken medical dialogue systems, we present, to the best of our knowledge, the first spoken medical drug prescriptions corpus, named PxSLU. It contains 4 hours of transcribed and annotated dialogues of drug prescriptions in French acquired through an experiment with 55 participants experts and non-experts in prescriptions. We also present some experiments that demonstrate the interest of this corpus for the evaluation and development of medical dialogue systems.