Search Results for author:
Found 9 papers, 6 papers with code
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition
In this study, based on the knowledge distillation framework and multi-task learning, we introduce the similarity metric model as an auxiliary task to improve the cross-lingual NER performance on the target domain.
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models
Enhancing the interpretability of text classification models can help increase the reliability of these models in real-world applications.
Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime
Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training.
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification
Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes.
A Hierarchical N-Gram Framework for Zero-Shot Link Prediction
Thus, in this paper, we propose a Hierarchical N-Gram framework for Zero-Shot Link Prediction (HNZSLP), which considers the dependencies among character n-grams of the relation surface name for ZSLP.
Neural Dialogue State Tracking with Temporally Expressive Networks
Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue.
Parallel Interactive Networks for Multi-Domain Dialogue State Generation
In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
