Summary of Msner: a Multilingual Speech Dataset For Named Entity Recognition, by Quentin Meeus and Marie-francine Moens and Hugo Van Hamme
MSNER: A Multilingual Speech Dataset for Named Entity Recognition
by Quentin Meeus, Marie-Francine Moens, Hugo Van hamme
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces MSNER, a multilingual speech corpus annotated with named entities, addressing the gap in spoken language understanding for Named Entity Recognition (NER). It provides annotations to the VoxPopuli dataset in four languages and an efficient annotation tool that leverages automatic pre-annotations. The corpus contains 590 hours of silver-annotated speech for training and validation, alongside a 17-hour evaluation set. Baseline NER models are also presented to stimulate further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Named Entity Recognition (NER) is important in understanding spoken language, but it’s mostly been studied using text data. This paper helps fix that by creating a big dataset of speech recordings from different languages. The recordings have names and other entities marked out, so AI models can learn to recognize them. The team also made a tool to help people annotate the data more quickly. They’re sharing all this with researchers so they can improve NER models. |
Keywords
» Artificial intelligence » Language understanding » Named entity recognition » Ner
