Papers by Xiaobing Zhao
Improving Low-resource Question Answering by Augmenting Question Information (2023.findings-emnlp)
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Andong Chen, Yuan Sun, Xiaobing Zhao, Rosella Galindo Esparza, Kehai Chen, Yang Xiang, Tiejun Zhao, Min Zhang
| Challenge: | Low-resource questions pose a significant challenge within the field of Question-Answering (QA) tasks. |
| Approach: | They propose a method that leverages large models' internal knowledge to enhance the quality of augmented data by Prompt Answer, Question Generation, and Question Filter. |
| Outcome: | The proposed method outperforms existing augmentation strategies on high-resource QA tasks like SQUAD1.1 and TriviaQA. |
Scaling Laws or Threshold Effects: Exploring the Optimal Vocabulary Size for Balancing Performance and Efficiency in Low-Resource Languages (2026.findings-acl)
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| Challenge: | vocab expansion scaling laws are well-established for high-resource languages, but they remain unverified in low-resourced settings. |
| Approach: | They propose to scale trilingual vocabulary for languages with 140 to 195,000 tokens . they find that BBPE follows a "decline-then-rise" pattern, whereas BPE improves monotonically . |
| Outcome: | The proposed configuration reduces pre-training duration by over 71% across 1.5B to 8B models while improving downstream performance. |
Enhancing Cross-Lingual Transfer through Reversible Transliteration: A Huffman-Based Approach for Low-Resource Languages (2025.acl-long)
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| Challenge: | Large language models demonstrate cross-lingual transfer capabilities, but these capabilities often fail to extend to low-resource languages, especially those utilizing non-Latin scripts. |
| Approach: | They propose to combine character transliteration with Huffman coding to create a complete transliterations framework that can be extended to other low-resource languages. |
| Outcome: | The proposed framework reduces storage requirements and improves accuracy and accuracy across multiple downstream tasks while maintaining performance on high-resource languages. |
Question Generation Based on Grammar Knowledge and Fine-grained Classification (2022.coling-1)
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| Challenge: | Recent research on question generation has achieved great success, but some question types and answers did not match. |
| Approach: | They construct a question type classifier and a query generator to solve the problem of question types not matching with other questions. |
| Outcome: | The proposed model improves the accuracy of interrogative words in generated questions. |