4 accepted papers
In real-world scenarios, due to data scarcity, cross-domain slot filling in spoken language understanding remains a significant challenge. Previous works focus on supplementing sequence labeling models with slot meta-information or metric learning. They have poor generalization capabilities lacking…
In dialogue intent detection, the challenge of acquiring sufficient corpora and the high cost of manual annotation often lead to incorrectly labeled or unrepresentative samples, which can hinder the generalization ability of classification models. Additionally, as using large language models for gen
In real-world scenarios, cross-domain slot filling in spoken language understanding remains a significant challenge due to data scarcity. Previous works exhibit limited generalization ability in the target domain, demonstrating effective knowledge transfer only on seen slots while performing poorly…
Pre-trained Language Models (PLMs) have achieved significant success in text classification. However, they still face challenges with hard samples, which refer to instances where the model exhibits diminished confidence in distinguishing new samples. Existing research has addressed related issues, b…