Misleading Relation Classifiers by Substituting Words in Texts (2023.findings-acl)
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| Challenge: | Existing methods to generate adversarial examples for relation classification are vulnerable to adversarials. |
| Approach: | They propose a method that uses most important parts of speech to substitute words with synonyms or hyponyms to generate adversarial texts of high quality. |
| Outcome: | The proposed method can generate adversarial texts of high quality and most relationships can be correctly identified in the process of human evaluation. |
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| Challenge: | Existing methods for relation extraction ignore the incompleteness of existing knowledge bases . current methods are too weak and cause noises when training and testing are not based on training data. |
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| Challenge: | Recent large language models have achieved state-of-the-art performance on many NLP tasks, but they rely on shortcut features and are unreliable when put under pressure. |
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READ: Improving Relation Extraction from an ADversarial Perspective (2024.findings-naacl)
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| Challenge: | Recent work in relation extraction (RE) has high generalization capability, but adversarial training methods rely on entities. |
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| Challenge: | Existing methods for relation classification exploit monolingual data due to lack of annotated data in other languages. |
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| Challenge: | State-of-the-art methods for relation classification suffer from data sparsity issue greatly. |
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Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis (2022.naacl-main)
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Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi
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Global Relation Embedding for Relation Extraction (N18-1)
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| Challenge: | Existing methods to extract textual relations with distant supervision are limited by their reliance on supervised training data. |
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Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing (2022.emnlp-main)
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| Challenge: | Existing entity typing models are subject to spurious correlations due to shortcuts and biased training. |
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Relation Induction in Word Embeddings Revisited (C18-1)
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| Challenge: | Existing approaches to relation induction are based on vector translations, but they are often inadequate for knowledge base completion. |
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