SEACrowd/mysentence

mySentence is a corpus with a total size of around 55K for Myanmar sentence segmentation. In formal Burmese (Myanmar language), sentences are grammatically structured and typically end with the "။" pote-ma symbol. However, informal language, more commonly used in daily conversations due to its natural flow, does not always follow predefined rules for ending sentences, making it challenging for machines to identify sentence boundaries. In this corpus, each token of the sentences and paragraphs is tagged from start to finish.

Languages

mya

Supported Tasks

Pos Tagging

Dataset Usage

Using datasets library

from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/mysentence", trust_remote_code=True)

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("mysentence", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("mysentence"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")

More details on how to load the seacrowd library can be found here.

Dataset Homepage

https://github.com/ye-kyaw-thu/mySentence

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Creative Commons Attribution Non Commercial Share Alike 4.0 (cc-by-nc-sa-4.0)

Citation

If you are using the Mysentence dataloader in your work, please cite the following:

@article{Aung_Kyaw_Thu_Hlaing_2023,
    title        = {{mySentence: Sentence Segmentation for Myanmar Language using Neural Machine Translation Approach}},
    author       = {Aung, Thura and Kyaw Thu , Ye and Hlaing , Zar Zar},
    year         = 2023,
    month        = {Nov.},
    journal      = {Journal of Intelligent Informatics and Smart Technology},
    volume       = 9,
    number       = {October},
    pages        = {e001},
    url          = {https://ph05.tci-thaijo.org/index.php/JIIST/article/view/87},
    place        = {Nonthaburi, Thailand},
    abstract     = {In the informal Myanmar language, for which most NLP applications are used, there is no predefined rule to mark the end of the sentence. Therefore, in this paper, we contributed the first Myanmar sentence segmentation corpus and systemat ically experimented with twelve neural sequence labeling architectures trained and tested on both sentence and sentence+paragraph data. The word LSTM + Softmax achieved the highest accuracy of 99.95{\%} while trained and tested on sentence-only data and 97.40{\%} while trained and tested on sentence + paragraph data.}
}
@inproceedings{10.1007/978-3-031-36886-8_24,
    title        = {{Neural Sequence Labeling Based Sentence Segmentation for Myanmar Language}},
    author       = {Thu, Ye Kyaw and Aung, Thura and Supnithi, Thepchai},
    year         = 2023,
    booktitle    = {The 12th Conference on Information Technology and Its Applications},
    publisher    = {Springer Nature Switzerland},
    address      = {Cham},
    pages        = {285--296},
    isbn         = {978-3-031-36886-8},
    editor       = {Nguyen, Ngoc Thanh and Le-Minh, Hoa and Huynh, Cong-Phap and Nguyen, Quang-Vu},
    abstract     = {In the informal Myanmar language, for which most NLP applications are used, there is no predefined rule to mark the end of the sentence. Therefore, in this paper, we contributed the first Myanmar sentence segmentation corpus and systemat ically experimented with twelve neural sequence labeling architectures trained and tested on both sentence and sentence+paragraph data. The word LSTM + Softmax achieved the highest accuracy of 99.95{\%} while trained and tested on sentence-only data and 97.40{\%} while trained and tested on sentence + paragraph data.}
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
    year={2024},
    eprint={2406.10118},
    journal={arXiv preprint arXiv: 2406.10118}
}