{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/indonli-a-natural-language-inference-dataset","title":"IndoNLI: A Natural Language Inference Dataset for Indonesian","arxiv_id":"2110.14566","date":"2021-10-27","proceeding":"EMNLP 2021 11","authors":["Rahmad Mahendra","Alham Fikri Aji","Samuel Louvan","Fahrurrozi Rahman","Clara Vania"],"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.","url_abs":"https://arxiv.org/abs/2110.14566v1","url_pdf":"https://arxiv.org/pdf/2110.14566v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"indonli-a-natural-language-inference-dataset","repo_url":"https://github.com/ir-nlp-csui/indonli","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"spatial-reasoning","task_name":"Spatial Reasoning"},{"task_slug":"xlm-r","task_name":"XLM-R"}],"methods":[{"method_slug":"test","method_name":"Test"},{"method_slug":"xlm-r","method_name":"XLM-R"}],"datasets_introduced":[{"slug":"indonli","name":"IndoNLI","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2110.14566","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}