open-source framework

We present the Cezanne-ai challenge and we will try to convince you to use our Conversational AI open-source framework by using:

1. your own code,
2. own language,
3. desired domains,
4. with your existing data (limited or not - please check the data-prerequisites file in this repository; be aware that not all the datasets/corpuses are mandatory).

In order to assist, we give you the following:

1. an architecture that was designed to cover complex objectives in conversational AI (based on the research paper: "Cezanne-ai: a conversational AI framework for emerging languages and limited data" - Coman et al.,2021 - see uploaded Cezanne.ai paper in the project documentation repository)
2. 50+1 pipelines described step-by-step (see the dummy code of this repository and the repository: Project-documentation. The additional pipeline (SPCA: sentence-intent) can either be requested: florincoman15@gmail.com, either be developed from scratch - see Cezanne-ai paper for intuition)
3. we will work together with you at the implementation (check out the Cezanne-ai complete repositories. We will try to keep up with you.),
4. we will share good practices and more efficient implementations from other contributors.

The framework consists of 3 main layers that can be implemented independently depending on your NLP/Conversational AI tasks:

1. Natural Input Understanding (an extension of NLU) with 2 sub-layers: Machine Education and Machine Learning

NIU-NLU - Machine Education architecture.pdf

NIU-NLU - Machine Learning architecture.pdf

2. Conversational Policy Learning (an extension of DPL)

CPL architecture.pdf

3. Natural Output Generation (an extension of NLG)

NOG-NLG architecture.pdf

This three layers have in total 50 pipelines (not all mandatory, but recommended to have a fundamental background and cover also complex objectives in the conversation user-bot)

COMPLYING WITH THE SAFETY ISSUES (related to the AGI implications) PRESENTED IN THE SUBSEQUENT FILE OF THE REPOSITORY IS BINDING!