most citedBERTSel: Answer Selection with Pre-trained Models

12 citations · 12 across the 1 of their papers we have counts for

collaborators

6 papers

cs.IR20224 cited

SeDR: Segment Representation Learning for Long Documents Dense Retrieval

Junying Chen, Qingcai Chen, Dongfang Li +1

Recently, Dense Retrieval (DR) has become a promising solution to document retrieval, where document representations are used to perform effective and efficient semantic search. Ho…

cs.CL2022

Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates

Dongfang Li, Baotian Hu, Qingcai Chen

Calibration strengthens the trustworthiness of black-box models by producing better accurate confidence estimates on given examples. However, little is known about if model explana…

cs.CL20226 cited

Prompt-based Text Entailment for Low-Resource Named Entity Recognition

Dongfang Li, Baotian Hu, Qingcai Chen

Pre-trained Language Models (PLMs) have been applied in NLP tasks and achieve promising results. Nevertheless, the fine-tuning procedure needs labeled data of the target domain, ma…

cs.CL2021

You Can Do Better! If You Elaborate the Reason When Making Prediction

Dongfang Li, Jingcong Tao, Qingcai Chen +1

Neural predictive models have achieved remarkable performance improvements in various natural language processing tasks. However, most neural predictive models suffer from the lack…

cs.CL2019

Semi-supervised Visual Feature Integration for Pre-trained Language Models

Lisai Zhang, Qingcai Chen, Dongfang Li +1

Integrating visual features has been proved useful for natural language understanding tasks. Nevertheless, in most existing multimodal language models, the alignment of visual and…

cs.CL201912 cited

BERTSel: Answer Selection with Pre-trained Models

Dongfang Li, Yifei Yu, Qingcai Chen +1

Recently, pre-trained models have been the dominant paradigm in natural language processing. They achieved remarkable state-of-the-art performance across a wide range of related ta…