TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	EXTRA DATA	REMOVE
Slot Filling	SLURP	Multi-SLURP	F1	0.642	# 3
Intent Classification	SLURP	Multi-SLURP	Accuracy (%)	78.33	# 3

Badge	Markdown
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/slurp-a-spoken-language-understanding-1/slot-filling-on-slurp)](https://paperswithcode.com/sota/slot-filling-on-slurp?p=slurp-a-spoken-language-understanding-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/slurp-a-spoken-language-understanding-1/intent-classification-on-slurp)](https://paperswithcode.com/sota/intent-classification-on-slurp?p=slurp-a-spoken-language-understanding-1)`

SLURP: A Spoken Language Understanding Resource Package

EMNLP 2020 · Emanuele Bastianelli, Andrea Vanzo, Pawel Swietojanski, Verena Rieser ·

Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https: //github.com/pswietojanski/slurp.