Zero-shot Slot Filling

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (21 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
  18 Apr 2022 6 repositories listed Syntology ran 1 of 10 samples · 9 unverified · 1 pointer-only (licence)
  We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation.
• Robust Retrieval Augmented Generation for Zero-shot Slot Filling
  31 Aug 2021 2 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI.
• Zero-shot Slot Filling with DPR and RAG
  17 Apr 2021 2 repositories listed
  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.
• Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling
  23 Oct 2023 1 repository listed
  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
  13 Oct 2023 1 repository listed
  To alleviate this issue, we present a novel Hierarchical Contrastive Learning Framework (HiCL) for zero-shot slot filling.
• Re2G: Retrieve, Rerank, Generate
  13 Jul 2022 1 repository listed Syntology ran 1 of 8 samples · 7 unverified
  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
  2 Jul 2022 1 repository listed
  Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations.
• Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling
  7 Oct 2021 1 repository listed
  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.
• GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling
  13 Jun 2021 1 repository listed
  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.
• Robust Zero-Shot Cross-Domain Slot Filling with Example Values
  17 Jun 2019 1 repository listed
  Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.