Selamat Datang!
IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian NLP dataset encompassing a broad range of morpho-syntactic, semantic, and discourse analysis competencies. Like GLUE Benchmark, The purpose of IndoLEM is to benchmark progress in Indonesian NLP. The tasks in IndoLEM can be categorized into one of these followings:
- ♦ Morpho-syntax and Sequence Labelling Tasks
- Part-of-speech (POS) tagging
- Named entity recognition (NER)
- Dependency parsing
- ♦ Semantic Tasks
- Sentiment Analysis
- Summarization
- ♦ Discourse Coherence Tasks
- Next Tweet Prediction
- Tweet Ordering
Fajri Koto, Afshin, Rahimi, Jey Han Lau, and Timothy Baldwin. IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP. In Proceedings of the 28th COLING, December 2020.