Chengyue Jiang

Chengyue Jiang, Wenyang Hui (Equal Contribution), Yong Jiang, Xiaobin Wang, Pengjun Xie, Kewei Tu “Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing”. (Paper)

Weiqi Wu, Chengyue Jiang, Yong Jiang, Xiaobin Wang, Pengjun Xie, Kewei Tu “Do PLMs Know and Understand Ontological Knowledge?”. (Paper)

Chengyue Jiang, Yong Jiang, Weiqi Wu, Yuting Zheng, Pengjun Xie, Kewei Tu “COMBO: A Complete Benchmark for Open KG Canonicalization”. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. (Paper) (Code)

Chengyue Jiang, Yong Jiang, Weiqi Wu, Zhongqiang Huang, Pengjun Xie, Kewei Tu “Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field”. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. (Paper) (Code)

Chengyue Jiang, Zijian Jin, Kewei Tu. “Neuralizing Regular Expressions for Slot Filling”. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. (Paper) (Code)

Chengyue Jiang, Yinggong Zhao, Shanbo Chu, Libin Shen, and Kewei Tu. “Cold-start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks”. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. (Paper) (Code)

Chengyue Jiang, Zhonglin Nian, Kaihao Guo, Shanbo Chu, Yinggong Zhao, Libin Shen, and Kewei Tu. “Learning Neumeral Embeddings”. In Findings of the Association for Computational Linguistics: EMNLP 2020. (Paper) (Code)

Xinyu Wang, Yixian Liu, Zixia Jia, Chengyue Jiang, and Kewei Tu. “ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing”. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL 2019). (Paper) (Poster)

Doing some exploration about large language model (LLM).

Doing some research about large language model (LLM) and information extraction. Act as the core developer of the entity typing part of the Adaseq Project in DAMO academy.

Doing some early research on using image captioning to help fault detection. Using simple sequence to sequence architecture, and implemented using Keras.

Software engineer internship. Participating in developing a webapp using Django backend and MySQL. The webapp is a simple cloud platform, used for managing costumers.

Adaseq is the open source sequence understanding libarary presented by DAMO NLP. It contains SOTA Named Entity Recognition, Entity Typing, Relation Extraction models. I act as the core developer of the entity typing part of the Adaseq Project in DAMO academy.
Implemente and replicate entity typing models including multi-label MLC, NPCRF, MCCE, Prompt Learning, etc.

The source codes(RE2NN,RE2NN-SEQ) of my papers “Cold-start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks” and “Neuralizing Regular Expressions for Slot Filling”, reaches over 130 stars on GitHub. The repos change regular expressions for text classification and slot filling into neural networks.