王卓尔 Zhuoer Wang

Selected Publications

1. AAAI

   RES: A Replicability Estimation System

   Zhuoer Wang, Qizhang Feng, Mohinish Chatterjee, Xing Zhao, Yezi Liu, Yuening Li, Abhay Kumar Singh, Frank M. Shipman, Xia Hu, and James Caverlee

   In AAAI 2022

   Abs HTML

   Reliable and faithful research is the cornerstone of breakthrough advancements and disruptive innovations. Assessing the credibility of scientific findings and claims in research publications has long been a time-consuming and challenging task for researchers and decision-makers. In this paper, we introduce RES - an intelligent system that assists humans in analyzing the credibility of scientific findings and claims in research publications in the field of social and behavioral sciences by estimating their replicability. The pipeline of RES consists of four major modules that perform feature extraction, replicability estimation, result explanation, and sentiment analysis respectively. Our evaluation based on human experts’ assessments suggests that the RES has achieved adequate performance. The RES is also built with a Graphical User Interface (GUI) that is publicly accessible at https://tamu-infolab.github.io/RES

2. EMNLP

   PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge

   Yun He, Zhuoer Wang, Yin Zhang, Ruihong Huang, and James Caverlee

   In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Nov 2020

   Abs Bib PDF Code

   We present a new benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge. PARADE contains paraphrases that overlap very little at the lexical and syntactic level but are semantically equivalent based on computer science domain knowledge, as well as non-paraphrases that overlap greatly at the lexical and syntactic level but are not semantically equivalent based on this domain knowledge. Experiments show that both state-of-the-art neural models and non-expert human annotators have poor performance on PARADE. For example, BERT after fine-tuning achieves an F1 score of 0.709, which is much lower than its performance on other paraphrase identification datasets. PARADE can serve as a resource for researchers interested in testing models that incorporate domain knowledge. We make our data and code freely available.

   @inproceedings{he-etal-2020-parade,
     title = {{PARADE}: {A} {N}ew {D}ataset for {P}araphrase {I}dentification {R}equiring {C}omputer {S}cience {D}omain {K}nowledge},
     author = {He, Yun and Wang, Zhuoer and Zhang, Yin and Huang, Ruihong and Caverlee, James},
     booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
     month = nov,
     year = {2020},
     address = {Online},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2020.emnlp-main.611},
     doi = {10.18653/v1/2020.emnlp-main.611},
     pages = {7572--7582},
     abbr = {EMNLP},
     bibtex_show = {true},
     pdf = {2020.emnlp-main.611.pdf},
     code = {https://github.com/heyunh2015/PARADE_dataset},
     selected = {true}
   }