Exploiting Textual Adversarial Attacks in Named Entity Recognition: A New Perspective

In this paper, we explore the challenges of few-shot named entity recognition (NER) and how it can be improved. Few-shot NER is a task where a model is trained to recognize entities in a new language or domain with only a few examples. This is a difficult problem because the model needs to learn to recognize entities quickly and accurately, without having enough data to learn from.
One approach to improving few-shot NER is through the use of information bottleneck (IB). IB is a technique that helps the model focus on the most important information when learning new tasks. This can help the model improve its performance on few-shot NER. However, directly applying IB to few-shot NER can actually make the model less robust to textual adversarial attacks.
Another challenge with few-shot NER is that it can be difficult to balance accuracy and generalization. The model needs to be accurate enough to recognize entities in new languages or domains, but it also needs to be able to generalize to new situations. This is a trade-off that the model must make, and finding the right balance is important for good performance.
To address these challenges, we propose a new approach called MINER. MINER uses an information theoretical perspective to improve few-shot NER. It does this by using a deep multi-view information bottleneck to learn a compact representation of entities that can be used for recognition. This approach improves the model’s robustness to textual adversarial attacks and also helps it generalize better to new situations.
In summary, few-shot NER is a challenging task because the model needs to learn to recognize entities quickly and accurately in new languages or domains with limited data. One approach to improving few-shot NER is through the use of information bottleneck, but this can make the model less robust to textual adversarial attacks. Our proposed approach, MINER, uses an information theoretical perspective to improve few-shot NER and also helps it generalize better to new situations.