Abstract
Ancient Chinese Reading Comprehension (ACRC) is challenging for the absence of datasets and the difficulty of understanding ancient languages. Further, among ACRC, entire-span-regarded (Entire spaN regarDed, END) questions are especially exhausting because of the input-length limitation of seminal BERTs, which solve modern-language reading comprehension expeditiously. To alleviate the datasets absence issue, this paper builds a new dataset ACRE (Ancient Chinese Reading-comprehension End-question). To tackle long inputs, this paper proposes a non-trivial model which is based on the convolution of multiple encoders that are BERT decedents, named EVERGREEN (EVidence-first bERt encodinG with entiRE-tExt coNvolution). Besides proving the effectiveness of encoding compressing via convolution, our experimental results also show that, for ACRC, first, neither pre-trained AC language models nor long-text-oriented transformers realize its value; second, the top evidence sentence along with distributed sentences are better than top-n evidence sentences as inputs of EVERGREEN; third, comparing with its variants, including dynamic convolution and multi-scale convolution, classical convolution is the best.
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Notes
- 1.
Both ACRE and the source code of EVERGREEN will be released on GitHub after publication.
- 2.
See eea.gd.gov.cn.
- 3.
The model of GuwenBERT is available on the github.
- 4.
- 5.
- 6.
Thanks to the anonymous NeurIPS reviewer. Although we can draw a line at the Chinese renaissance around 1920 as the boundary between ancient and modern Chinese, fictions which are written in or after the Ming dynasty are not in this scope.
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Acknowledgements
This paper is supported by Guangdong Basic and Applied Basic Research Foundation, China (Grant No. 2021A1515012556).
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Rao, D., Huang, G., Jiang, Z. (2024). Ancient Chinese Machine Reading Comprehension Exception Question Dataset with a Non-trivial Model. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14326. Springer, Singapore. https://doi.org/10.1007/978-981-99-7022-3_14
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