About
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.
Benchmarks
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Subtasks
Datasets
Greatest papers with code
Learning End-to-End Goal-Oriented Dialog
We show similar result patterns on data extracted from an online concierge service.
Data Programming: Creating Large Training Sets, Quickly
NeurIPS 2016
• HazyResearch/snorkel
• 
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.
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.
Ranked #3 on
Fact Verification
on KILT: FEVER
ENTITY LINKING FACT CHECKING FACT VERIFICATION OPEN-DOMAIN DIALOG OPEN-DOMAIN QUESTION ANSWERING SLOT FILLING
Neural Baby Talk
CVPR 2018
• jiasenlu/NeuralBabyTalk
•
We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image.
Position-aware Attention and Supervised Data Improve Slot Filling
EMNLP 2017
• yuhaozhang/tacred-relation
•
The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.
Ranked #4 on
Relation Extraction
on Re-TACRED
KNOWLEDGE BASE POPULATION KNOWLEDGE GRAPHS RELATION EXTRACTION SLOT FILLING
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
NAACL 2018
• MiuLab/SlotGated-SLU
• 
Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights.
Ranked #6 on
Intent Detection
on SNIPS
GLOBAL OPTIMIZATION INTENT DETECTION SLOT FILLING SPOKEN DIALOGUE SYSTEMS SPOKEN LANGUAGE UNDERSTANDING
Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP
We introduce Baseline: a library for reproducible deep learning research and fast model development for NLP.
LANGUAGE MODELLING MACHINE TRANSLATION NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING SLOT FILLING
BERT for Joint Intent Classification and Slot Filling
Intent classification and slot filling are two essential tasks for natural language understanding.
CLASSIFICATION INTENT CLASSIFICATION NATURAL LANGUAGE UNDERSTANDING SLOT FILLING
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.
Ranked #3 on
Intent Detection
on ATIS
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.