IndoNLI: A Natural Language Inference Dataset for Indonesian

• URL: http://arxiv.org/abs/2110.14566v1
• Date: Wed, 27 Oct 2021 16:37:13 GMT
• Title: IndoNLI: A Natural Language Inference Dataset for Indonesian
• Authors: Rahmad Mahendra, Alham Fikri Aji, Samuel Louvan, Fahrurrozi Rahman, and Clara Vania
• Abstract summary: IndoNLI is the first human-elicited NLI dataset for Indonesian. We collect nearly 18K sentence pairs annotated by crowd workers and experts.
• Score: 4.707529518839985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present IndoNLI, the first human-elicited NLI dataset for Indonesian. We adapt the data collection protocol for MNLI and collect nearly 18K sentence pairs annotated by crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. Experiment results show that XLM-R outperforms other pre-trained models in our data. The best performance on the expert-annotated data is still far below human performance (13.4% accuracy gap), suggesting that this test set is especially challenging. Furthermore, our analysis shows that our expert-annotated data is more diverse and contains fewer annotation artifacts than the crowd-annotated data. We hope this dataset can help accelerate progress in Indonesian NLP research.

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