Papers by Zixuan Ren
Language Model Pre-Training with Sparse Latent Typing (2022.emnlp-main)
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| Challenge: | Modern large-scale Pre-trained Language Models focus on text reconstruction, but have not sought to learn latent-level interpretable representations of sentences. |
| Approach: | They propose a new pre-training objective that enables the model to learn latent types . the objective allows the model a self-supervised way to extract sentence-level keywords . |
| Outcome: | The proposed model learns interpretable latent type categories without external knowledge and improves downstream tasks. |
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)
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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | None. None.. None! |
| Approach: | None. None.. None! |
| Outcome: | None. None. No. : |
Nested Event Extraction upon Pivot Element Recognition (2024.lrec-main)
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Weicheng Ren, Zixuan Li, Xiaolong Jin, Long Bai, Miao Su, Yantao Liu, Saiping Guan, Jiafeng Guo, Xueqi Cheng
| Challenge: | Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. |
| Approach: | They propose a new model that extracts nested events mainly based on recognizing PEs. |
| Outcome: | The proposed model can extract nested events based on recognizing PEs . it incorporates information from both event types and argument roles to improve performance . |
Towards Informative Open-ended Text Generation with Dynamic Knowledge Triples (2023.findings-emnlp)
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| Challenge: | Pretrained language models (PLMs) have impressive capabilities in open-ended text generation. |
| Approach: | They propose a dynamic knowledge-guided informative open-ended text generation approach that utilizes a knowledge graph to help the model generate more contextually related entities and detailed facts. |
| Outcome: | The proposed approach generates more informative texts than baselines. |