slot-filling
108 papers with code • 1 benchmarks • 3 datasets
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
MASSIVE
Most implemented papers
Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models
PragmaticsLab/CodeSwitchingAdversarial
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This is in line with the common understanding of how multilingual models conduct transferring between languages
A Persian Benchmark for Joint Intent Detection and Slot Filling
To evaluate the effectiveness of our benchmark, we employ state-of-the-art methods for intent detection and slot filling.
Multilingual Relation Extraction using Compositional Universal Schema
patverga/torch-relation-extraction
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NAACL 2016
In response, this paper introduces significant further improvements to the coverage and flexibility of universal schema relation extraction: predictions for entities unseen in training and multilingual transfer learning to domains with no annotation.
Evaluating Induced CCG Parsers on Grounded Semantic Parsing
We compare the effectiveness of four different syntactic CCG parsers for a semantic slot-filling task to explore how much syntactic supervision is required for downstream semantic analysis.
Neural Models for Sequence Chunking
threelittlemonkeys/pointer-network-pytorch
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Many natural language understanding (NLU) tasks, such as shallow parsing (i. e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence.
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling
zliucr/coach
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While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems.
Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation
One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data.
Neural Baby Talk
jiasenlu/NeuralBabyTalk
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CVPR 2018
We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image.
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
pangolulu/DCMTL
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Slot filling is a critical task in natural language understanding (NLU) for dialog systems.
TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data.
