Papers by Shaohua Zhang
Rethinking Masked Language Modeling for Chinese Spelling Correction (2023.acl-long)
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| Challenge: | Existing CSC models over-fit the error model while under-fitting the language model, resulting in poor generalization to out-of-distribution error patterns. |
| Approach: | They propose to use a multi-domain benchmark LEMON to assess the open domain generalization of Chinese Spelling Correction models. |
| Outcome: | The proposed method achieves state-of-the-art results on SIGHAN, ECSpell, and LEMON. |
MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on evaluating single-round interactions, neglecting other critical aspects. |
| Approach: | They propose a benchmark to evaluate full-duplex speech language models in multi-round settings . they segment continuous full-dual dialogues into discrete turns for evaluation . |
| Outcome: | The proposed benchmark compared full-duplex speech language models with full-dual speech models . the results show that the models perform better in multi-round settings than standard models compared to benchmarks . |
Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine (2025.acl-industry)
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Zhuoxuan Jiang, Tianyang Zhang, Peiyan Peng, Jing Chen, Yinong Xun, Haotian Zhang, Lichi Li, Yong Li, Shaohua Zhang
| Challenge: | Compared to math word problems, geometry problems emphasize multi-modal formats and the translation between informal and formal languages. |
| Approach: | They propose a symbolic deduction engine-based geometry problem generation framework that leverages a symbolic deduction engine to generate geometry problems. |
| Outcome: | The proposed method avoids inherent biases in translating natural language into formal language and guarantees to control the generated problems in terms of knowledge points and difficulties by an elaborate checking function. |
Spelling Error Correction with Soft-Masked BERT (2020.acl-main)
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| Challenge: | Experimental results show that the proposed method is significantly better than the baselines including the one solely based on BERT. |
| Approach: | They propose a neural architecture which uses a network for error detection and a system for error correction based on BERT, with the latter connected to the other using what they call soft-masking technique. |
| Outcome: | The proposed method performs better than baselines including the one solely based on BERT, and is general and may be employed in other language detection-correction problems. |