MSNER: A Multilingual Speech Dataset for Named Entity Recognition
This addresses the problem of limited resources for spoken language NER, particularly for non-English languages, though it is incremental as it builds on existing datasets.
The paper tackles the lack of multilingual speech datasets for Named Entity Recognition by introducing MSNER, a freely available corpus with annotations in four languages, resulting in 590 hours of silver-annotated training data and a 17-hour manually-annotated evaluation set.
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.