NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis
We build a Nepali benchmark from two annotators who labelled the same sentences blind.
ProofPaper ↗
Key points
- Many sentiment datasets use one annotator for each item, so the test labels show the conventions of that person.
- They labelled 5,614 sentences from tweets of Nepali public figures on a five-point scale.
- On the three-class benchmark, NepBERTa with a fixed tokenizer gets a macro-F1 of 0.877 and IndicBERTv2 0.867.
- The default tokenizers of three Nepali BERT models delete the non-spacing Devanagari vowel signs and the virama.
Sources (1)
- [1]NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment AnalysisarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 08:54 PM
We build a Nepali benchmark from two annotators who labelled the same sentences blind.
Many sentiment datasets use one annotator for each item, so the test labels show the conventions of that person.
Extractive summary: sentences quoted from the sources.
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