Shengjie Li

4^th-year CS PhD student

University of Texas at Dallas

sxl180006 [at] hlt.utdallas.edu

I’m currently a fourth-year Ph.D. student in Computer Science at the University of Texas at Dallas, under the esteemed guidance of Prof. Vincent Ng. Prior to this, I obtained my master’s degree in Computer Science from Rutgers University and my bachelor’s degree in Software Engineering from South China Agricultural University.

My research primarily focuses on Natural Language Processing. Over the course of my academic journey, I’ve explored areas including Multimodal NLP, Entity Coreference Resolution, Discourse Deixis Resolution, Automated Essay Scoring, and Large Language Model Augmentation.

Education

Selected publications

Cross-modal Coherence Modeling for Caption Generation

Malihe Alikhani, Piyush Sharma, Shengjie Li, Radu Soricut, and Matthew Stone

In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020

Abs Bib

We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning. Using an annotation protocol specifically devised for capturing image–caption coherence relations, we annotate 10,000 instances from publicly-available image–caption pairs. We introduce a new task for learning inferences in imagery and text, coherence relation prediction, and show that these coherence annotations can be exploited to learn relation classifiers as an intermediary step, and also train coherence-aware, controllable image captioning models. The results show a dramatic improvement in the consistency and quality of the generated captions with respect to information needs specified via coherence relations.
@inproceedings{alikhani-etal-2020-cross, title = {Cross-modal Coherence Modeling for Caption Generation}, author = {Alikhani, Malihe and Sharma, Piyush and Li, Shengjie and Soricut, Radu and Stone, Matthew}, year = {2020}, month = jul, booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, publisher = {Association for Computational Linguistics}, address = {Online}, pages = {6525--6535}, doi = {10.18653/v1/2020.acl-main.583}, url = {https://aclanthology.org/2020.acl-main.583}, }

End-to-End Neural Discourse Deixis Resolution in Dialogue

Shengjie Li, and Vincent Ng

In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Dec 2022

Abs Bib

We adapt Lee et al.’s (2018) span-based entity coreference model to the task of end-to-end discourse deixis resolution in dialogue, specifically by proposing extensions to their model that exploit task-specific characteristics. The resulting model, dd-utt, achieves state-of-the-art results on the four datasets in the CODI-CRAC 2021 shared task.

@inproceedings{li-ng-2022-end,
  title = {End-to-End Neural Discourse Deixis Resolution in Dialogue},
  author = {Li, Shengjie and Ng, Vincent},
  year = {2022},
  month = dec,
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  publisher = {Association for Computational Linguistics},
  address = {Abu Dhabi, United Arab Emirates},
  pages = {11322--11334},
  doi = {10.18653/v1/2022.emnlp-main.778},
  url = {https://aclanthology.org/2022.emnlp-main.778},
}

Multimodal Propaganda Processing

Vincent Ng, and Shengjie Li

In Proceedings of the AAAI Conference on Artificial Intelligence, Sep 2023

Abs Bib

Propaganda campaigns have long been used to influence public opinion via disseminating biased and/or misleading information. Despite the increasing prevalence of propaganda content on the Internet, few attempts have been made by AI researchers to analyze such content. We introduce the task of multimodal propaganda processing, where the goal is to automatically analyze propaganda content. We believe that this task presents a long-term challenge to AI researchers and that successful processing of propaganda could bring machine understanding one important step closer to human understanding. We discuss the technical challenges associated with this task and outline the steps that need to be taken to address it.
@article{Ng_Li_2023, title = {Multimodal Propaganda Processing}, author = {Ng, Vincent and Li, Shengjie}, year = {2023}, month = sep, journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, volume = {37}, number = {13}, pages = {15368--15375}, doi = {10.1609/aaai.v37i13.26792}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/26792}, }

ICLE++: A Corpus for Holistic and Fine-Grained Trait-Specific Scoring of Persuasive Student Essays

Shengjie Li, and Vincent Ng

To appear in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2024

Abs Bib

@inproceedings{Li_Ng_2024,
  title = {ICLE++: A Corpus for Holistic and Fine-Grained Trait-Specific Scoring of Persuasive Student Essays},
  author = {Li, Shengjie and Ng, Vincent},
  booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  month = jun,
  year = {2024},
  publisher = {Association for Computational Linguistics},
  address = {Mexico City, Mexico},
  pubstate = {forthcoming},
  pages = {},
  url = {},
}

Automated Essay Scoring: Recent Successes and Future Directions

Shengjie Li, and Vincent Ng

To appear in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, Aug 2024

Survey Track

Bib

@inproceedings{Li_Ng_ijcai2024,
  title = {Automated Essay Scoring: Recent Successes and Future Directions},
  author = {Li, Shengjie and Ng, Vincent},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
                 Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  pages = {},
  year = {2024},
  month = aug,
  note = {Survey Track},
  doi = {},
  url = {},
  pubstate = {forthcoming},
}

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