Boer Lyu


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Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction
Boer Lyu | Lu Chen | Kai Yu
Findings of the Association for Computational Linguistics: EMNLP 2021

Sememes are defined as the atomic units to describe the semantic meaning of concepts. Due to the difficulty of manually annotating sememes and the inconsistency of annotations between experts, the lexical sememe prediction task has been proposed. However, previous methods heavily rely on word or character embeddings, and ignore the fine-grained information. In this paper, we propose a novel pre-training method which is designed to better incorporate the internal information of Chinese character. The Glyph enhanced Chinese Character representation (GCC) is used to assist sememe prediction. We experiment and evaluate our model on HowNet, which is a famous sememe knowledge base. The experimental results show that our method outperforms existing non-external information models.


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Neural Graph Matching Networks for Chinese Short Text Matching
Lu Chen | Yanbin Zhao | Boer Lyu | Lesheng Jin | Zhi Chen | Su Zhu | Kai Yu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Chinese short text matching usually employs word sequences rather than character sequences to get better performance. However, Chinese word segmentation can be erroneous, ambiguous or inconsistent, which consequently hurts the final matching performance. To address this problem, we propose neural graph matching networks, a novel sentence matching framework capable of dealing with multi-granular input information. Instead of a character sequence or a single word sequence, paired word lattices formed from multiple word segmentation hypotheses are used as input and the model learns a graph representation according to an attentive graph matching mechanism. Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models.