Nankai University
B.Eng. Intelligent Science and Technology
Sept. 2015 - June 2019
Gangrong Jiang is currently a graduate student at USC. His interests lie in reasoning and natural language generation. Specifically, he has experience in text summarization, question answering in unstructured and semi-structured data, reinforcement learning, and knowledge graph reasoning. His current research focuses on label-efficient deep learning and interpretable AI.
Sept. 2019 - Oct. 2020
Research Assistant | Advisor: Prof. Sean (Xiang) Ren
Los Angeles, CA
June 2020 - Aug. 2020
Software Engineer Intern
Mountain View, CA
Sept. 2017 - June 2019
Research Assistant | Advisor: Prof. Zhenglu Yang
Tianjin, China
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning
The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
@inproceedings{lei-etal-2020-learning, title = "Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning", author = "Lei, Deren and Jiang, Gangrong and Gu, Xiaotao and Sun, Kexuan and Mao, Yuning and Ren, Xiang", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.688", doi = "10.18653/v1/2020.emnlp-main.688", pages = "8541--8547", abstract = "Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during a traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets shows that RuleGuider clearly improves the performance of walk-based models without losing interpretability.", }
Self-Supervised Relation Learning for Knowledge Graph Reasoning
In Submission
Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters
The 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20), 2020
@article{Zeng_Lei_Li_Jiang_Ferrara_Zyda_2020, title={Learning to Reason in Round-Based Games: Multi-Task Sequence Generation for Purchasing Decision Making in First-Person Shooters}, volume={16}, url={https://ojs.aaai.org/index.php/AIIDE/article/view/7446}, abstractNote={<p class="abstract">Sequential reasoning is a complex human ability, with extensive previous research focusing on gaming AI in a single continuous game, round-based decision makings extending to a sequence of games remain less explored. Counter-Strike: Global Offensive (CS:GO), as a round-based game with abundant expert demonstrations, provides an excellent environment for multi-player round-based sequential reasoning. In this work, we propose a Sequence Reasoner with Round Attribute Encoder and Multi-Task Decoder to interpret the strategies behind the round-based purchasing decisions. We adopt few-shot learning to sample multiple rounds in a match, and modified model agnostic meta-learning algorithm Reptile for the meta-learning loop. We formulate each round as a multi-task sequence generation problem. Our state representations combine action encoder, team encoder, player features, round attribute encoder, and economy encoders to help our agent learn to reason under this specific multi-player round-based scenario. A complete ablation study and comparison with the greedy approach certify the effectiveness of our model. Our research will open doors for interpretable AI for understanding episodic and long-term purchasing strategies beyond the gaming community.</p>}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment}, author={Zeng, Yilei and Lei, Deren and Li, Beichen and Jiang, Gangrong and Ferrara, Emilio and Zyda, Michael}, year={2020}, month={Oct.}, pages={308-314} }
Neural Module Teacher: Learning to Answer Questions with Compositional Explanation
In Submission
TSAS: A Transformer-based Model for Sentiment-Aware Abstractive Summarization
Technical Report, 2018