Zero-shot Slot Filling
10 papers with code • 3 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Zero-shot Slot Filling
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T-REx
Latest papers
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.
HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot Filling
ai-agi/hicl
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To alleviate this issue, we present a novel Hierarchical Contrastive Learning Framework (HiCL) for zero-shot slot filling.
Re2G: Retrieve, Rerank, Generate
ibm/kgi-slot-filling
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NAACL 2022
As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
lefteris12/multitask-schema-guided-dst
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Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations.
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
alexa/massive
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We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation.
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling
w-lw/pclc
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EMNLP 2021
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.
Robust Retrieval Augmented Generation for Zero-shot Slot Filling
ibm/kgi-slot-filling
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EMNLP 2021
Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI.
GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling
shikib/generative_slot_filling
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SIGDIAL (ACL) 2021
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.
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 Zero-Shot Cross-Domain Slot Filling with Example Values
zliucr/coach
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ACL 2019
Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains.
