BERTScore: Evaluating Text Generation with BERT

267 indexed citations
published 2020

Impact in

Classified as

Authors
Tianyi ZhangFelix WuKilian Q. WeinbergerYoav Artzi
Journal
arXiv (Cornell University)

In The Last Decade

doi.org/w8995737 →

Countries where authors are citing BERTScore: Evaluating Text Generation with BERT

Specialization
Citations

This map shows the geographic impact of BERTScore: Evaluating Text Generation with BERT. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by BERTScore: Evaluating Text Generation with BERT with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites BERTScore: Evaluating Text Generation with BERT more than expected).

Fields of papers citing BERTScore: Evaluating Text Generation with BERT

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of BERTScore: Evaluating Text Generation with BERT. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the BERTScore: Evaluating Text Generation with BERT.

About BERTScore: Evaluating Text Generation with BERT

This paper, published in 2020, received 267 indexed citations . Written by Tianyi Zhang, Felix Wu, Kilian Q. Weinberger and Yoav Artzi covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (257 citations), Computer Vision and Pattern Recognition (67 citations), Information Systems (17 citations), Molecular Biology (13 citations) and Signal Processing (5 citations). Published in arXiv (Cornell University).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w8995737.

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