language:ind license:cc-by-4.0 sentence-ordering arxiv:2011.00677 region:us
IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.
This task is based on the sentence ordering task of Barzilay and Lapata (2008) to assess text relatedness. We construct the data by shuffling Twitter threads (containing 3 to 5 tweets), and assessing the predicted ordering in terms of rank correlation (p) with the original. The experiment is based on 5-fold cross validation.

Train: 4327 threads
Development: 760 threads
Test: 1521 threads
@article{DBLP:journals/corr/abs-2011-00677,
  author    = {Fajri Koto and
               Afshin Rahimi and
               Jey Han Lau and
               Timothy Baldwin},
  title     = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
               Model for Indonesian {NLP}},
  journal   = {CoRR},
  volume    = {abs/2011.00677},
  year      = {2020},
  url       = {https://arxiv.org/abs/2011.00677},
  eprinttype = {arXiv},
  eprint    = {2011.00677},
  timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
---
license: cc-by-4.0
tags:
- sentence-ordering
language:
- ind
---

# indolem_tweet_ordering

IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.

This task is based on the sentence ordering task of Barzilay and Lapata (2008) to assess text relatedness. We construct the data by shuffling Twitter threads (containing 3 to 5 tweets), and assessing the predicted ordering in terms of rank correlation (p) with the original. The experiment is based on 5-fold cross validation.



Train: 4327 threads

Development: 760 threads

Test: 1521 threads

## Dataset Usage

Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.

## Citation

```
@article{DBLP:journals/corr/abs-2011-00677,
  author    = {Fajri Koto and
               Afshin Rahimi and
               Jey Han Lau and
               Timothy Baldwin},
  title     = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
               Model for Indonesian {NLP}},
  journal   = {CoRR},
  volume    = {abs/2011.00677},
  year      = {2020},
  url       = {https://arxiv.org/abs/2011.00677},
  eprinttype = {arXiv},
  eprint    = {2011.00677},
  timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

## License

Creative Commons Attribution 4.0

## Homepage

[https://indolem.github.io/](https://indolem.github.io/)

### NusaCatalogue

For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)