The Deep Learning group’s mission is to advance the state-of-the-art on deep learning and its application to natural language processing, computer vision, multi-modal intelligence, and for making progress on conversational AI. Our research interests are:
- Neural language modeling for natural language understanding and generation. Some ongoing projects are MT-DNN, UniLM, DeBERTa, question-answering, long text generation, etc.
- Neural symbolic computing. We are developing next-generation architectures to bridge gap between neural and symbolic representations with neural symbols. Some ongoing projects are relational encoding using Tensor-Product Representations, AI for Code, etc.
- Vision-language grounding and understanding. Some ongoing projects are VinVL, OSCAR, vision-language pre-training, vision language navigation, image editing and generation, image commenting and captioning, etc.
- Conversational AI. Some ongoing projects are conversation learner and SOLOIST which enable dialog authors to build task-oriented dialog systems at scale via machine teaching and transfer learning, ConvLab which is an open-source multi-domain dialog system platform, and response generation for social bots such as Microsoft XiaoIce, etc.