| Challenge: | Existing models with no effective open category data during training are limited by the lack of effective open categories data during the training stage. |
| Approach: | They propose an approach to generate effective open category samples in the training stage and without requiring prior knowledge or external datasets. |
| Outcome: | The proposed approach generates effective synthetic open category samples in the training stage and without requiring any prior knowledge or external datasets. |
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| Challenge: | Information Retrieval (IR) is fundamental to many modern NLP applications. |
| Approach: | They propose a taxonomy that categorizes negative sampling techniques in dense IR . they analyze them with respect to trade-offs between effectiveness, computational cost, implementation difficulty . |
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| Challenge: | Existing metrics that rely on comparisons to a set of known correct responses do not account for the variety of responses and therefore correlate poorly with human judgment. |
| Approach: | They propose a method of manipulating a golden response to create a new negative response that is designed to be inappropriate within the context while maintaining high similarity with the original golden response. |
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Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation (2021.findings-acl)
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| Challenge: | Open-domain neural dialogue models have achieved high performance in response ranking and evaluation tasks. |
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Open Set Relation Extraction via Unknown-Aware Training (2023.acl-long)
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Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, Xuanjing Huang
| Challenge: | Existing supervised relation extraction methods can still misclassify unknown relations into known relations due to the lack of supervision signals. |
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| Challenge: | Existing methods to synthesize training labels with labeling rules ignore data imbalance issue . weak supervision paradigm is often used to reduce human efforts to produce training labels inexpensively. |
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A Novel Negative Sample Generation Method for Contrastive Learning in Hierarchical Text Classification (2025.coling-main)
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| Challenge: | Existing methods for hierarchical text classification struggle with fine-grained labels, leading to difficulties in accurate classification. |
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NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases (2021.emnlp-main)
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| Challenge: | Recent advances in knowledge base construction techniques focus on the acquisition of positive (true) KB statements, but negative (false) statements are important for discriminative reasoning. |
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| Challenge: | Existing methods to generate implausible stories using plots are unnatural and oversimplify the characteristics of implusible machine-generated stories. |
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Sampling Better Negatives for Distantly Supervised Named Entity Recognition (2023.findings-acl)
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| Challenge: | Existing supervised named entity recognition approaches rely on human annotations. |
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Rethinking Negative Sampling for Handling Missing Entity Annotations (2022.acl-long)
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| Challenge: | Empirical studies show low missampling rate and high uncertainty are both essential for achieving promising performances with negative sampling. |
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