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SILICONE Benchmark

Introduced by Emile Chapuis et al. in Hierarchical Pre-training for Sequence Labelling in Spoken Dialog23 Sep 2020 archive 2025-07-28

The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. All datasets are in the English language and covers a large variety of domains (e.g daily life, scripted scenarios, joint task completion, phone call conversations, and televsion dialogue). Some datasets additionally include emotion and/or sentiment labels.

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Text Classification SILICONE Benchmark Pretrained Hierarchical Transformer 1:1 Accuracy 71.25 Hierarchical Pre-training for Sequence Labelling in Spoken Dialog — 1 Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 1. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog 0 1 23 Sep 2020 not harvested

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • SILICONE Benchmark

1 variant name, as the archive lists them.

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