sz128/slot_filling_and_intent_detection_of_SLU

slot filling, intent detection, joint training, ATIS & SNIPS datasets, the Facebook’s multilingual dataset, MIT corpus, E-commerce Shopping Assistant (ECSA) dataset, CoNLL2003 NER, ELMo, BERT, XLNet

50
/ 100
Established

This project helps build conversational AI systems by enabling them to understand user commands. It takes spoken or typed natural language input, like "book a flight from Denver to San Francisco," and breaks it down into distinct intents (e.g., 'flight booking') and specific details (e.g., 'Denver' as origin, 'San Francisco' as destination). This is designed for conversational AI developers or researchers who need to develop or experiment with core natural language understanding components for virtual assistants, chatbots, or voice interfaces.

400 stars. No commits in the last 6 months.

Use this if you are developing or researching methods to accurately extract user intentions and key information from natural language utterances for conversational AI applications.

Not ideal if you need a ready-to-deploy, end-to-end conversational AI platform, as this project focuses specifically on the underlying slot filling and intent detection tasks.

conversational-ai natural-language-understanding virtual-assistant chatbot-development speech-recognition
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 24 / 25

How are scores calculated?

Stars

400

Forks

103

Language

Python

License

Apache-2.0

Last pushed

Feb 04, 2021

Commits (30d)

0

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