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            <title>Proceedings of the 21st Chinese National Conference on Computational Linguistics</title>
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    <abstract>“Natural language understanding tasks require a comprehensive understanding of natural language and further reasoning about it, on the basis of holistic information at different levels to gain comprehensive knowledge. In recent years, pre-trained language models (PrLMs) have shown impressive performance in natural language understanding. However, they rely mainly on extracting context-sensitive statistical patterns without explicitly modeling linguistic information, such as semantic relationships entailed in natural language. In this work, we propose EventBERT, an event-based semantic representation model that takes BERT as the backbone and refines with event-based structural semantics in terms of graph convolution networks. EventBERT benefits simultaneously from rich event-based structures embodied in the graph and contextual semantics learned in pre-trained model BERT. Experimental results on the GLUE benchmark show that the proposed model consistently outperforms the baseline model.”</abstract>
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%0 Conference Proceedings
%T EventBERT: Incorporating Event-based Semantics for Natural Language Understanding
%A Anni, Zou
%A Zhuosheng, Zhang
%A Hai, Zhao
%Y Sun, Maosong
%Y Liu, Yang
%Y Che, Wanxiang
%Y Feng, Yang
%Y Qiu, Xipeng
%Y Rao, Gaoqi
%Y Chen, Yubo
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G English
%F anni-etal-2022-eventbert
%X “Natural language understanding tasks require a comprehensive understanding of natural language and further reasoning about it, on the basis of holistic information at different levels to gain comprehensive knowledge. In recent years, pre-trained language models (PrLMs) have shown impressive performance in natural language understanding. However, they rely mainly on extracting context-sensitive statistical patterns without explicitly modeling linguistic information, such as semantic relationships entailed in natural language. In this work, we propose EventBERT, an event-based semantic representation model that takes BERT as the backbone and refines with event-based structural semantics in terms of graph convolution networks. EventBERT benefits simultaneously from rich event-based structures embodied in the graph and contextual semantics learned in pre-trained model BERT. Experimental results on the GLUE benchmark show that the proposed model consistently outperforms the baseline model.”
%U https://aclanthology.org/2022.ccl-1.69
%P 774-785

Markdown (Informal)

[EventBERT: Incorporating Event-based Semantics for Natural Language Understanding](https://aclanthology.org/2022.ccl-1.69) (Anni et al., CCL 2022)

• EventBERT: Incorporating Event-based Semantics for Natural Language Understanding (Anni et al., CCL 2022)

ACL

• Zou Anni, Zhang Zhuosheng, and Zhao Hai. 2022. EventBERT: Incorporating Event-based Semantics for Natural Language Understanding. In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 774–785, Nanchang, China. Chinese Information Processing Society of China.