Few-shot text classification has extensive application where the sample collection is
expensive or complicated. When the penalty for classification errors is high, such as early …

Few-shot intent detection is a more challenging application. However, traditional prototypical
networks based on averaging often suffer from issues such as missing key information, poor …

User intents are ever-changing, which requires deep learning models to have the ability to
classify unknown intents. Meta-learning aims to solve this problem by improving the model's …

The meta-learning paradigm has demonstrated significant effectiveness in few-shot text
classification. Currently, numerous efforts are grounded in metric-based learning, utilizing …

Few-shot learning has gained much interest and achieved remarkable performance in
handling limited data scenarios. However, existing few-shot text classification methods …

The phenomenon of cyberviolence has become a critical issue in online security, drawing
attention from various stakeholders. A major shortcoming in the previous works is the …

Few-shot intent detection is a challenging task. Most existing methods only focus on
acquisition of generalization knowledge in known classes, or on the adaptation situation of …

Meta-learning has emerged as a prominent technology for few-shot text classification and
has achieved promising performance. However, existing methods often encounter difficulties …

Meta-learning has shown remarkable success in few-shot learning, and a popular metric-
based meta-learning method known as prototypical network has gained widespread …

This multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of
the European Conference on Machine Learning and Knowledge Discovery in Databases …