{
 "id": "SEACrowd/ted_en_id",
 "owner": "SEACrowd",
 "name": "ted_en_id",
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 "last_modified": "2024-06-24T13:24:00.000Z",
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 "tasks": [
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 "citation": "@inproceedings{qi2018and,\n  title={When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},\n  author={Qi, Ye and Sachan, Devendra and Felix, Matthieu and Padmanabhan, Sarguna and Neubig, Graham},\n  booktitle={Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},\n  pages={529--535},\n  year={2018}\n}\n\n@inproceedings{cahyawijaya-etal-2021-indonlg,\n    title = \"{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation\",\n    author = \"Cahyawijaya, Samuel  and\n      Winata, Genta Indra  and\n      Wilie, Bryan  and\n      Vincentio, Karissa  and\n      Li, Xiaohong  and\n      Kuncoro, Adhiguna  and\n      Ruder, Sebastian  and\n      Lim, Zhi Yuan  and\n      Bahar, Syafri  and\n      Khodra, Masayu  and\n      Purwarianti, Ayu  and\n      Fung, Pascale\",\n    booktitle = \"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing\",\n    month = nov,\n    year = \"2021\",\n    address = \"Online and Punta Cana, Dominican Republic\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.emnlp-main.699\",\n    doi = \"10.18653/v1/2021.emnlp-main.699\",\n    pages = \"8875--8898\",\n    abstract = \"Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly av",
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}