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Microsoft Research NLP Projects

This is a list of open-sourced projects Microsoft Research NLP Group involved. (ranked in time order)

Datasets

Title Description Related projects
Dialogue Feedback Dataset 100+ Millions of dialogues with corresponding human feedback to learn which one gets better feedback DialogRPT
Grounded Dialogue Dataset Dialogues with information grounded in external knowledge, e.g. wikipedia pages DSTC7, CMR
Reddit Dialogue Dataset 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017 DialoGPT

Papers

Title Links Notes Tags
Dialogue Response Ranking Training with Large-Scale Human Feedback Data code/model/data, demo EMNLP 2020 dialog ranking
POINTER: Constrained Text Generation via Insertion-based Generative Pre-training code, demo EMNLP 2020 generation
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space code, demo EMNLP 2020 generation
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers code ACL 2020 parsing, sql
A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks code ACL 2020 multimodal
INSET: Sentence Infilling with INter-SEntential Transformer code/demo ACL 2020 generation
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation code/model/data ACL 2020 dialog generation
MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform code ACL 2020 dialog generation framework knowledge style
Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention code/data CVPR 2019 navigation imitation learning
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading code/model/data ACL 2019 knowledge dialog generation
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling code ACL 2019 dialog generation framework
Structuring Latent Spaces for Stylized Response Generation code/data EMNLP 2019 style dialog generation
Jointly Optimizing Diversity and Relevance in Neural Response Generation code/data NAACL 2019 dialog generation
Towards Content Transfer through Grounded Text Generation code/data NAACL 2019 generation knowledge

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Legal Notices

Microsoft and any contributors grant you a license to the Microsoft documentation and other content in this repository under the Creative Commons Attribution 4.0 International Public License, see the LICENSE file, and grant you a license to any code in the repository under the MIT License, see the LICENSE-CODE file.

Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries. The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks. Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653.

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Microsoft and any contributors reserve all other rights, whether under their respective copyrights, patents, or trademarks, whether by implication, estoppel or otherwise.

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msr-nlp-projects's Issues

Optimus VAE support

Hello,

I would really like to experiment with the optimus model. I can't afford a gpu cloud instance all the time and colab doesn't allow docker. Can you release an arm compiled version so i could try to run this on a jetson gpu? Broke college art student who's into "data art" here. Perhaps you guys have a suggestion on how to get the long gpu runtimes cheaply enough given the circumstances.

Any help would be appreciated -> you might just help to inspire.

Best,

Aidan Collins

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