Papers by Xanh Ho
How Well Do Multi-hop Reading Comprehension Models Understand Date Information? (2022.aacl-short)
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| Challenge: | Existing multi-hop reading comprehension datasets have reasoning shortcuts that can be used to answer comparison questions without performing multi- hop reasoning. |
| Approach: | They propose a dataset with three probing tasks in addition to the main question . they then evaluate the model's ability to understand date information . |
| Outcome: | The proposed model performs well in date comparison and number subtraction tasks. |
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering (2023.findings-eacl)
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| Challenge: | Existing studies have utilized underlying reasoning (UR) tasks in multi-hop question answering datasets to explain the predicted answers and evaluate models' reasoning abilities. |
| Approach: | They analyze UR tasks in QA datasets to determine their effectiveness . they find that UR task is helpful in preventing reasoning shortcuts . |
| Outcome: | The proposed model improves QA performance, reasoning shortcuts, and robustness on adversarial questions. |
Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers (2025.findings-emnlp)
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| Challenge: | predicting the final label alone is insufficient and offers limited interpretability. |
| Approach: | They propose to reframe table–text alignment as an explanation task requiring models to identify the table cells essential for claim verification. |
| Outcome: | The proposed taxonomy improves claim verification performance and most LLMs fail to recover human-aligned rationales, suggesting that their predictions do not stem from faithful reasoning. |
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps (2020.coling-main)
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| Challenge: | Existing multi-hop question answering datasets do not provide a complete explanation for the reasoning process from the question to the answer. |
| Approach: | They propose a multi-hop question answering dataset that uses structured and unstructured data to test reasoning skills. |
| Outcome: | The proposed dataset ensures multi-hop reasoning while being challenging for multi-models. |