Summary of Noisebench: Benchmarking the Impact Of Real Label Noise on Named Entity Recognition, by Elena Merdjanovska et al.
NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition
by Elena Merdjanovska, Ansar Aynetdinov, Alan Akbik
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a new benchmark for named entity recognition (NER) called NoiseBench, which contains six types of real noise, including expert errors, crowdsourcing errors, automatic annotation errors, and LLM errors. The authors show that this real noise is significantly more challenging than the simulated noise used in previous studies, and that current state-of-the-art models for noise-robust learning fall short of their theoretically achievable upper bound. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Named entity recognition (NER) is a crucial task in natural language processing. However, existing training data often contains incorrect labels which can negatively impact model quality. This paper proposes NoiseBench, a new benchmark that simulates real-world noise by corrupting clean training data with six different types of errors. The authors show that this real noise is more challenging than simulated noise and highlight the need for better noise-robust learning approaches. |
Keywords
» Artificial intelligence » Named entity recognition » Natural language processing » Ner
