Slot Filling
94 papers with code • 10 benchmarks • 18 datasets
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.
Source: Real-time On-Demand Crowd-powered Entity Extraction
Image credit: Robust Retrieval Augmented Generation for Zero-shot Slot Filling
Benchmarks
These leaderboards are used to track progress in Slot Filling
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Libraries
Use these libraries to find Slot Filling models and implementations
ATIS
SNIPS
SGD
KILT
Polyvore
xSID
MEDIA
Most implemented papers
BERT for Joint Intent Classification and Slot Filling
Intent classification and slot filling are two essential tasks for natural language understanding.
Learning End-to-End Goal-Oriented Dialog
We show similar result patterns on data extracted from an online concierge service.
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition.
Data Programming: Creating Large Training Sets, Quickly
HazyResearch/snorkel
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NeurIPS 2016
Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.
Learning Dense Representations of Phrases at Scale
jhyuklee/DensePhrases
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ACL 2021
Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).
Joint Slot Filling and Intent Detection via Capsule Neural Networks
czhang99/Capsule-NLU
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ACL 2019
Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
google-research-datasets/dstc8-schema-guided-dialogue
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In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains.
KILT: a Benchmark for Knowledge Intensive Language Tasks
We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.
A Knowledge-Grounded Neural Conversation Model
We generalize the widely-used Seq2Seq approach by conditioning responses on both conversation history and external "facts", allowing the model to be versatile and applicable in an open-domain setting.
Zero-Shot Relation Extraction via Reading Comprehension
We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot.
