MSNER: A Multilingual Speech Dataset for Named Entity Recognition

TL;DR

This paper introduces MSNER, a multilingual speech dataset with annotated named entities across four languages, along with tools and baseline models to advance spoken language NER research.

Contribution

It provides the first multilingual speech corpus for NER, with efficient annotation tools and baseline models to facilitate future research.

Findings

Created 590 and 15 hours of silver-annotated speech data

Developed an annotation tool leveraging automatic pre-annotations

Provided baseline NER models for the dataset

Abstract

While extensively explored in text-based tasks, Named Entity Recognition (NER) remains largely neglected in spoken language understanding. Existing resources are limited to a single, English-only dataset. This paper addresses this gap by introducing MSNER, a freely available, multilingual speech corpus annotated with named entities. It provides annotations to the VoxPopuli dataset in four languages (Dutch, French, German, and Spanish). We have also releasing an efficient annotation tool that leverages automatic pre-annotations for faster manual refinement. This results in 590 and 15 hours of silver-annotated speech for training and validation, alongside a 17-hour, manually-annotated evaluation set. We further provide an analysis comparing silver and gold annotations. Finally, we present baseline NER models to stimulate further research on this newly available dataset.