To appear in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2024
@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={},}
@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},}
@inproceedings{li-ng-2024-Conundrums,title={Conundrums in Cross-Prompt Automated Essay Scoring: Making Sense of the State of the Art},author={Li, Shengjie and Ng, Vincent},editor={},booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},month=aug,year={2024},address={Bangkok, Thailand},publisher={Association for Computational Linguistics},url={},doi={},pages={},pubstate={forthcoming},}
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},}
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},}
We present the systems that we developed for all three tracks of the CODI-CRAC 2022 shared task, namely the anaphora resolution track, the bridging resolution track, and the discourse deixis resolution track. Combining an effective encoding of the input using the SpanBERT_\textLarge encoder with an extensive hyperparameter search process, our systems achieved the highest scores in all phases of all three tracks.
@inproceedings{li-etal-2022-neural-anaphora,title={Neural Anaphora Resolution in Dialogue Revisited},author={Li, Shengjie and Kobayashi, Hideo and Ng, Vincent},year={2022},month=oct,booktitle={Proceedings of the CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue},publisher={Association for Computational Linguistics},address={Gyeongju, Republic of Korea},pages={32--47},url={https://aclanthology.org/2022.codi-crac.4},}
The CODI-CRAC 2021 shared task is the first shared task that focuses exclusively on anaphora resolution in dialogue and provides three tracks, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. We perform a cross-task analysis of the systems that participated in the shared task in each of these tracks.
@inproceedings{li-etal-2021-codi,title={The {CODI}-{CRAC} 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis Resolution in Dialogue: A Cross-Team Analysis},author={Li, Shengjie and Kobayashi, Hideo and Ng, Vincent},year={2021},month=nov,booktitle={Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue},publisher={Association for Computational Linguistics},address={Punta Cana, Dominican Republic},pages={71--95},doi={10.18653/v1/2021.codi-sharedtask.8},url={https://aclanthology.org/2021.codi-sharedtask.8},}
We describe the systems that we developed for the three tracks of the CODI-CRAC 2021 shared task, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. Our team ranked second for entity coreference resolution, first for bridging resolution, and first for discourse deixis resolution.
@inproceedings{kobayashi-etal-2021-neural,title={Neural Anaphora Resolution in Dialogue},author={Kobayashi, Hideo and Li, Shengjie and Ng, Vincent},year={2021},month=nov,booktitle={Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue},publisher={Association for Computational Linguistics},address={Punta Cana, Dominican Republic},pages={16--31},doi={10.18653/v1/2021.codi-sharedtask.2},url={https://aclanthology.org/2021.codi-sharedtask.2},}
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},}