Papers with relational tasks
Injecting Relational Structural Representation in Neural Networks for Question Similarity (P18-2)
Copied to clipboard
| Challenge: | Recent years have seen exponential growth and use of web forums, where users can exchange and find information just asking questions in natural language. |
| Approach: | They propose to use Tree Kernels to learn a model on relatively few pairs of questions as gold standard (GS) predicting labels on a very large corpus of question pairs is also a useful approach, they propose . |
| Outcome: | The proposed model can learn more accurate models after fine tuning on GS. |
Language Models Implement Simple Word2Vec-style Vector Arithmetic (2024.naacl-long)
Copied to clipboard
| Challenge: | a primary criticism of language models is their inscrutability. |
| Approach: | They propose to use a vector arithmetic style mechanism to solve relational tasks . they find that this mechanism is specific to tasks that require retrieval from pretraining memory . |
| Outcome: | The proposed model reduces to a simple additive update for a variety of tasks . the findings contribute to proving that the models are interpretable and reliable . |
DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for learning relational knowledge are replay-based and prioritize data uniformly . a pronounced bias towards new tasks can be caused by the introduction of new tasks . |
| Approach: | They propose a framework that decouples the process of prior information preservation and new knowledge acquisition. |
| Outcome: | Extensive experiments show that the framework outperforms baselines across two datasets. |
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs (2026.acl-long)
Copied to clipboard
Yihua Zhu, Qianying Liu, Jiaxin Wang, Fei Cheng, Chaoran Liu, Akiko Aizawa, Sadao Kurohashi, Hidetoshi Shimodaira
| Challenge: | Autoregressive LLMs perform well on relational tasks that require linking entities via relational words, but it is unclear whether they learn the logical semantics of such relations or whether left-to-right order bias is involved. |
| Approach: | They propose a framework that generates text from symmetric/inverse triples and trains autoregressive models from scratch. |
| Outcome: | The proposed framework generates text from symmetric/inverse triples, trains autoregressive models from scratch, and evaluates memorization, logical inference, and in-context generalization to unseen entities. |