GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
- Journal
- International Conference on Learning Representations
In The Last Decade
doi.org/w5246005 →Countries where authors are citing GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
This map shows the geographic impact of GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. 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 GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding more than expected).
Fields of papers citing GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
This network shows the impact of GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.
About GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
This paper, published in 2018, received 630 indexed citations . Written by Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy and Samuel R. Bowman covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (605 citations), Computer Vision and Pattern Recognition (230 citations), Information Systems (35 citations), Molecular Biology (18 citations) and Sociology and Political Science (16 citations). Published in International Conference on Learning Representations.
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/w5246005.