Slot Filling
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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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.
Neural Baby Talk
CVPR 2018
• jiasenlu/NeuralBabyTalk
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We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image.
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
Position-aware Attention and Supervised Data Improve Slot Filling
EMNLP 2017
• yuhaozhang/tacred-relation
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The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.
#11 best model for
Relation Extraction
on 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
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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.
INTENT DETECTION SLOT FILLING SPOKEN DIALOGUE SYSTEMS SPOKEN LANGUAGE UNDERSTANDING
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.
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
This allows a single dialogue system to easily support a large number of services and facilitates simple integration of new services without requiring additional training data.
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.
Natural Language Does Not Emerge `Naturally' in Multi-Agent Dialog
EMNLP 2017
• batra-mlp-lab/lang-emerge
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A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, learned without any human supervision!