Hongye Tan


2020

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A Frame-based Sentence Representation for Machine Reading Comprehension
Shaoru Guo | Ru Li | Hongye Tan | Xiaoli Li | Yong Guan | Hongyan Zhao | Yueping Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge. To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling. Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema. Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations. Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.

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Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension
Shaoru Guo | Yong Guan | Ru Li | Xiaoli Li | Hongye Tan
Proceedings of the 28th International Conference on Computational Linguistics

Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding. It is surprising that jointly considering syntax and semantics in neural networks was never formally reported in literature. This paper makes the first attempt by proposing a novel Syntax and Frame Semantics model for Machine Reading Comprehension (SS-MRC), which takes full advantage of syntax and frame semantics to get richer text representation. Our extensive experimental results demonstrate that SS-MRC performs better than ten state-of-the-art technologies on machine reading comprehension task.

2014

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Detection on Inconsistency of Verb Phrase in TreeBank
Chaoqun Duan | Dequan Zheng | Conghui Zhu | Sheng Li | Hongye Tan
Proceedings of The Third CIPS-SIGHAN Joint Conference on Chinese Language Processing

2008

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A Chinese Word Segmentation System Based on Cascade Model
Jianfeng Zhang | Jiaheng Zheng | Hu Zhang | Hongye Tan
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing