Chenyang Li


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WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models
Jun-Yan He | Zhi-Qi Cheng | Chenyang Li | Jingdong Sun | Wangmeng Xiang | Xianhui Lin | Xiaoyang Kang | Zengke Jin | Yusen Hu | Bin Luo | Yifeng Geng | Xuansong Xie
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis, relying on the Large Language Model (LLM). The system incorporates four key modules: the LLM Engine, SemTypo, StyTypo, and TexTypo modules. 1) The LLM Engine, empowered by the LLM (e.g. GPT-3.5), interprets user inputs and generates actionable prompts for the other modules, thereby transforming abstract concepts into tangible designs. 2) The SemTypo module optimizes font designs using semantic concepts, striking a balance between artistic transformation and readability. 3) Building on the semantic layout provided by the SemTypo module, the StyTypo module creates smooth, refined images. 4) The TexTypo module further enhances the design’s aesthetics through texture rendering, enabling the generation of inventive textured fonts. Notably, WordArt Designer highlights the fusion of generative AI with artistic typography. Experience its capabilities on ModelScope:

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CCL23-Eval 任务6系统报告:基于深度学习的电信网络诈骗案件分类(System Report for CCL23-Eval Task 6: Classification of Telecom Internet Fraud Cases Based on Deep Learning)
Chenyang Li (李晨阳) | Long Zhang (张龙) | Zhongjie Zhao (赵中杰) | Hui Guo (郭辉)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)



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Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting
Manisha Srivastava | Yichao Lu | Riley Peschon | Chenyang Li
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

One main challenge in building task-oriented dialogue systems is the limited amount of supervised training data available. In this work, we present a method for training retrieval-based dialogue systems using a small amount of high-quality, annotated data and a larger, unlabeled dataset. We show that pretraining using unlabeled data can bring better model performance with a 31% boost in Recall@1 compared with no pretraining. The proposed finetuning technique based on a small amount of high-quality, annotated data resulted in 26% offline and 33% online performance improvement in Recall@1 over the pretrained model. The model is deployed in an agent-support application and evaluated on live customer service contacts, providing additional insights into the real-world implications compared with most other publications in the domain often using asynchronous transcripts (e.g. Reddit data). The high performance of 74% Recall@1 shown in the customer service example demonstrates the effectiveness of this pretrain-finetune approach in dealing with the limited supervised data challenge.