https://scholars.lib.ntu.edu.tw/handle/123456789/631140
標題: | Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN | 作者: | Chen, Si An Liu, Jie Jyun Yang, Tsung Han HSUAN-TIEN LIN CHIH-JEN LIN |
公開日期: | 1-一月-2022 | 來源出版物: | NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference | 摘要: | The power and the potential of deep learning models attract many researchers to design advanced and sophisticated architectures. Nevertheless, the progress is sometimes unreal due to various possible reasons. In this work, through an astonishing example we argue that more efforts should be paid to ensure the progress in developing a new deep learning method. For a highly influential multi-label text classification method XML-CNN, we show that the superior performance claimed in the original paper was mainly due to some unbelievable coincidences. We re-examine XML-CNN and make a re-implementation which reveals some contradictory findings to the claims in the original paper. Our study suggests suitable baselines for multi-label text classification tasks and confirms that the progress on a new architecture cannot be confidently justified without a cautious investigation. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/631140 | ISBN: | 9781955917711 |
顯示於: | 資訊工程學系 |
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