1 code implementation • 26 Apr 2024 • WenHao Zhang, Mengqi Zhang, Shiguang Wu, Jiahuan Pei, Zhaochun Ren, Maarten de Rijke, Zhumin Chen, Pengjie Ren
However, in information retrieval community, there is little research on exclusionary retrieval, where users express what they do not want in their queries.
no code implementations • 27 Feb 2024 • Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten de Rijke, Zhumin Chen, Jiahuan Pei
Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models' scale and the diversity of tasks increase.
no code implementations • 13 Jul 2023 • Wentao Deng, Jiahuan Pei, Zhaochun Ren, Zhumin Chen, Pengjie Ren
Specifically, it improves 2. 06% and 1. 00% of F1 score on the two datasets, compared with the strongest baseline with only 5% labeled data.
1 code implementation • 27 Dec 2021 • Jiahuan Pei, Cheng Wang, György Szarvas
In this work, we propose a novel way to enable transformers to have the capability of uncertainty estimation and, meanwhile, retain the original predictive performance.
1 code implementation • 11 Oct 2021 • Benyou Wang, Qianqian Xie, Jiahuan Pei, Zhihong Chen, Prayag Tiwari, Zhao Li, Jie Fu
In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks.
1 code implementation • 1 Sep 2021 • Guojun Yan, Jiahuan Pei, Pengjie Ren, Zhaochun Ren, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen
(1) there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i. e., intents, actions, slots, values), and (2) there is no set of established benchmarks for MDSs for multi-domain, multi-service medical dialogues.
1 code implementation • 16 Feb 2021 • Jiahuan Pei, Pengjie Ren, Maarten de Rijke
We find that CoMemNN is able to enrich user profiles effectively, which results in an improvement of 3. 06% in terms of response selection accuracy compared to state-of-the-art methods.
2 code implementations • 19 Nov 2019 • Jiahuan Pei, Pengjie Ren, Christof Monz, Maarten de Rijke
We propose a novel mixture-of-generators network (MoGNet) for DRG, where we assume that each token of a response is drawn from a mixture of distributions.
1 code implementation • 10 Jul 2019 • Jiahuan Pei, Pengjie Ren, Maarten de Rijke
We propose a neural Modular Task-oriented Dialogue System(MTDS) framework, in which a few expert bots are combined to generate the response for a given dialogue context.
no code implementations • 16 Jun 2019 • Jiahuan Pei, Arent Stienstra, Julia Kiseleva, Maarten de Rijke
Obtaining key information from a complex, long dialogue context is challenging, especially when different sources of information are available, e. g., the user's utterances, the system's responses, and results retrieved from a knowledge base (KB).