Papers by Tianshu Lyu
On Length Divergence Bias in Textual Matching Models (2022.findings-acl)
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| Challenge: | Existing deep models have been successful in textual matching tasks, but it is unclear whether they understand language or measure semantic similarity of texts. |
| Approach: | They propose an adversarial evaluation scheme which invalidates the length divergence bias in TM datasets. |
| Outcome: | The proposed method improves the robustness and generalization ability of models at the same time. |