Zero-shot Slot Filling
10 papers with code • 3 benchmarks • 3 datasets
Most implemented papers
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation.
Zero-shot Slot Filling with DPR and RAG
Recently, there has been a promising direction in evaluating language models in the same way we would evaluate knowledge bases, and the task of slot filling is the most suitable to this intent.
Robust Retrieval Augmented Generation for Zero-shot Slot Filling
Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI.
Robust Zero-Shot Cross-Domain Slot Filling with Example Values
Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains.
GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling
We instead achieve strong alignment by simultaneously modifying both the pre-trained model and the formulation of the downstream task, which is more efficient and preserves the scalability of transfer learning.
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling
Zero-shot cross-domain slot filling alleviates the data dependence in the case of data scarcity in the target domain, which has aroused extensive research.
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations.
Re2G: Retrieve, Rerank, Generate
As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.
HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot Filling
To alleviate this issue, we present a novel Hierarchical Contrastive Learning Framework (HiCL) for zero-shot slot filling.
Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling
In practice, these dominant pipeline models may be limited in computational efficiency and generalization capacity because of non-parallel inference and context-free discrete label embeddings.