Papers by Yanming Sun
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion (2023.acl-long)
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Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie
| Challenge: | Existing methods for embedding knowledge graphs implicitly memorize relation rules to infer missing links, but they are difficult to memorize due to the inherent deficiencies of such implicit memorization strategy. |
| Approach: | They propose a vertical learning paradigm that allows to explicitly copy target information from related factual triples for more accurate prediction. |
| Outcome: | The proposed model improves generalization ability and makes distant link prediction significantly easier. |
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization (2023.emnlp-main)
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| Challenge: | Existing summarization benchmarks overlap in time with pre-training corpora and fine-tuning datasets. |
| Approach: | They propose a temporal generalization benchmark that contains data samples from 2010 to 2022 to understand the temporal ability of abstractive summarization models. |
| Outcome: | The proposed benchmark analyzes data samples from 2010 to 2022 to understand the temporal generalization ability of abstractive summarization models. |
G-IdiomAlign: A Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment (2026.acl-long)
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| Challenge: | Existing tools for cross-lingual idiom-to-idiom equivalence evaluation are limited . figurative meanings are non-compositional and culturally grounded, making literal mappings unreliable. |
| Approach: | They propose a gloss-pivoted benchmark where each idiom is anchored by an English gloss from Wiktionary. |
| Outcome: | The proposed benchmark is based on a dictionary-anchored English idiom . a bias to literal translation is a dominant failure mode across diverse LLMs, the study shows . |