Papers by Yi-Chung Lin
A Meaning-Based Statistical English Math Word Problem Solver (N18-1)
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| Challenge: | Experimental results show that the proposed approach understands the meaning of each quantity in the text more. |
| Approach: | They propose a meaning-based approach for solving English math word problems . they analyze text, transform body and question parts into corresponding logic forms . Statistical models are proposed to select operator and operands . |
| Outcome: | The proposed approach outperforms existing systems on benchmark and noisy datasets. |
How Fast can BERT Learn Simple Natural Language Inference? (2021.eacl-main)
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| Challenge: | Efficiency of learning of BERT is very slow due to hidden dataset bias . however, some studies show that it can learn with surface clues/patterns . |
| Approach: | They propose to use a simple entailment judgment case to test whether BERT can learn without hidden dataset bias. |
| Outcome: | The proposed case shows that BERT can learn without hidden bias without utilizing dataset bias. |