Yiqi Tong


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An Adaptive Prompt Generation Framework for Task-oriented Dialogue System
Jun Gao | Liuyu Xiang | Huijia Wu | Han Zhao | Yiqi Tong | Zhaofeng He
Findings of the Association for Computational Linguistics: EMNLP 2023

The de facto way of utilizing black-box large language models (LLMs) to perform various downstream tasks is prompting. However, obtaining suitable prompts for specific tasks is still a challenging problem. While existing LLM-based methods demonstrate promising performance in task-oriented dialogue (TOD) task, they often require manual adjustment in prompt selection, or focus solely on dialogue understanding or generation. To address these issues, we propose an adaptive prompt generation framework to fully unleash the potential of LLMs for the comprehensive TOD system. Firstly, we design a trainable slot generator (TSG) that can generate domain and slot information in the belief state, which serves as prior knowledge for subsequent prompt generation. Next, we propose an adaptive prompt generator (APG) that utilizes the prior knowledge to generate prompts for the LLM, deriving the belief state and system response of the dialogue for evaluation. Finally, we evaluate our framework on the MultiWOZ 2.0 dataset. Extensive experiments demonstrate that our method outperforms existing methods. Our code and data will be released.


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一种基于IDLSTM+CRF的中文主地域抽取方法(A Chinese Main Location Extraction Method based on IDLSTM+CRF)
Yiqi Tong (童逸琦) | Peigen Ye (叶培根) | Biao Fu (付彪) | Yidong Chen (陈毅东) | Xiaodong Shi (史晓东)
Proceedings of the 20th Chinese National Conference on Computational Linguistics


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A Multi-Task Approach for Improving Biomedical Named Entity Recognition by Incorporating Multi-Granularity information
Yiqi Tong | Yidong Chen | Xiaodong Shi
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information
Yiqi Tong | Jiangbin Zheng | Hongkang Zhu | Yidong Chen | Xiaodong Shi
Proceedings of the 28th International Conference on Computational Linguistics

Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence information is helpful for improving the performance of the NMT models, what information should be regarded as context remains ambiguous. To solve this problem, we proposed a novel cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. The experiments on NIST evaluation sets demonstrate that our proposed model achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the first to introduce theme-rheme theory into the field of machine translation.