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SotA/Natural Language Processing/Slot Filling

Slot Filling

31 benchmarks458 papers

The goal of Slot Filling is to identify from a running dialog different slots, which correspond to different parameters of the user’s query. For instance, when a user queries for nearby restaurants, key slots for location and preferred food are required for a dialog system to retrieve the appropriate information. Thus, the main challenge in the slot-filling task is to extract the target entity.

<span class="description-source">Source: Real-time On-Demand Crowd-powered Entity Extraction </span>

Image credit: Robust Retrieval Augmented Generation for Zero-shot Slot Filling

Benchmarks

Slot Filling on KILT: Zero Shot RE

KILT-ACR-PrecRecall@5AccuracyF1KILT-F1

Slot Filling on KILT: T-REx

KILT-ACR-PrecRecall@5AccuracyF1KILT-F1

Slot Filling on MixSNIPS

Micro F1

Slot Filling on MixATIS

Micro F1

Slot Filling on ATIS

F1

Slot Filling on SNIPS

F1F1 (1-shot) avgF1 (5-shot) avg

Slot Filling on MASSIVE

Slot F1 Score

Slot Filling on T-REx

R-PrecR@5

Slot Filling on zsRE

R-PrecR@5

Slot Filling on SLURP

F1

Slot Filling on ATIS (vi)

Slot F1

Slot Filling on CAIS

F1

Slot Filling on Dialogue State Tracking Challenge

F1 score

Slot Filling on MULTIWOZ 2.2

F1 score

Slot Filling on Polyvore

FITB

Slot Filling on ProSLU

F1

Slot Filling on W-NUT 2020 Shared Task-3

F1