Yi Zheng
2021
Summarising Historical Text in Modern Languages
Xutan Peng
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Yi Zheng
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Chenghua Lin
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Advaith Siddharthan
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language. This is a fundamentally important routine to historians and digital humanities researchers but has never been automated. We compile a high-quality gold-standard text summarisation dataset, which consists of historical German and Chinese news from hundreds of years ago summarised in modern German or Chinese. Based on cross-lingual transfer learning techniques, we propose a summarisation model that can be trained even with no cross-lingual (historical to modern) parallel data, and further benchmark it against state-of-the-art algorithms. We report automatic and human evaluations that distinguish the historic to modern language summarisation task from standard cross-lingual summarisation (i.e., modern to modern language), highlight the distinctness and value of our dataset, and demonstrate that our transfer learning approach outperforms standard cross-lingual benchmarks on this task.
2010
Hedge Classification with Syntactic Dependency Features Based on an Ensemble Classifier
Yi Zheng
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Qifeng Dai
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Qiming Luo
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Enhong Chen
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task
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Co-authors
- Xutan Peng 1
- Chenghua Lin 1
- Advaith Siddharthan 1
- Qifeng Dai 1
- Qiming Luo 1
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