Papers by Kyung Seo Ki
Inspecting Soundness of AMR Similarity Metrics in terms of Equivalence and Inequivalence (2024.starsem-1)
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| Challenge: | Existing Abstract Meaning Representation (AMR) similarity metrics have less investigated their soundness . |
| Approach: | They propose a new experimental method to evaluate soundness of AMR similarity metrics in terms of equivalence and inequivalentity. |
| Outcome: | The proposed method satisfies the soundness criteria of existing AMR similarity metrics and improves them by proposing a revised metric, SMATCH . |
EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers (2022.acl-long)
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| Challenge: | Existing neural approaches to solve algebraic word problems have a plausible answer, but this belief has less been verified due to Q. |
| Approach: | They propose a neural model EPT-X which utilizes natural language explanations to solve an algebraic word problem. |
| Outcome: | The proposed model achieves an average performance of 69.59% on a PEN dataset and produces explanations with quality comparable to human output. |
Generating Equation by Utilizing Operators : GEO model (2020.coling-main)
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| Challenge: | Existing neural models that use hand-crafted features are expensive and lack domain-specific knowledge. |
| Approach: | They propose a GEO model that uses operator-based features to generate equations using natural language sentences. |
| Outcome: | The proposed model outperforms state-of-the-art models on two datasets and 82.1% in ALG514. |
Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model (2020.emnlp-main)
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| Challenge: | Existing models that generate solution equations using ‘Op (operator/operand) tokens suffered expression fragmentation and operand-context separation. |
| Approach: | They propose a pure neural model, Expression-Pointer Transformer, which uses (1) ‘Expression’ token and (2) operand-context pointers when generating solution equations. |
| Outcome: | The proposed model achieves comparable performance accuracy to state-of-the-art models and achieves better performance than existing models by at most 40%. |