The focus of our research is on the integration of natural language processing (NLP), machine learning algorithms, dialogue systems, and the knowledge graph. We are also aiming at building an NLP research platform that not only integrates current SOTA NLP models but is also capable of jointly learning with Computer Vision and Robotics perceptions. A candidate should have a strong interest in one or multiple of the following topics:

· Natural Language Grounding.
· Interpretable Reasoning Model for Question-Answering, including Neural-Symbolic Reasoning, Graph Reasoning, etc.
· Pragmatics Reasoning.
· Large-scale data pretraining. Transformer-based models, such as RoBERTa, BART, ALBERT, etc.
· Dialogue related topics. Natural Language Understanding (NLU), knowledge-graph based dialogue generation, dialogue management, empathic conversational agent.
· Embodied AI. Learning through interaction or communication with the environment.
· Machine Learning, including Deep Learning, Imitation Learning, Reinforcement Learning, etc.
· Other related fields in NLP, such as Neural Machine Translation (NMT), Neural Relation Extraction (NRE) etc.