Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good

被引:0
作者
Gonzalez, Fernando [1 ]
Jin, Zhijing [1 ,2 ]
Schoelkopf, Bernhard [1 ,2 ]
Hope, Tom [4 ,5 ]
Sachan, Mrinmaya [1 ]
Mihalcea, Rada [3 ]
机构
[1] Swiss Fed Inst Technol, Zurich, Switzerland
[2] MPI Intelligent Syst, Tubingen, Germany
[3] Univ Michigan, Ann Arbor, MI USA
[4] Hebrew Univ Jerusalem, Jerusalem, Israel
[5] Allen Inst AI AI2, Seattle, WA USA
来源
FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS - EMNLP 2023 | 2023年
基金
瑞士国家科学基金会;
关键词
ARTIFICIAL-INTELLIGENCE;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the recent advances in natural language processing (NLP), a vast number of applications have emerged across various use cases. Among the plethora of NLP applications, many academic researchers are motivated to do work that has a positive social impact, in line with the recent initiatives of NLP for Social Good (NLP4SG). However, it is not always obvious to researchers how their research efforts tackle today's big social problems. Thus, in this paper, we introduce NLP4SGPAPERS, a scientific dataset with three associated tasks that can help identify NLP4SG papers and characterize the NLP4SG landscape by: (1) identifying the papers that address a social problem, (2) mapping them to the corresponding UN Sustainable Development Goals (SDGs), and (3) identifying the task they solve and the methods they use. Using state-of-the-art NLP models, we address each of these tasks and use them on the entire ACL Anthology, resulting in a visualization workspace that gives researchers a comprehensive overview of the field of NLP4SG.(1)
引用
收藏
页码:415 / 438
页数:24
相关论文
共 52 条
  • [1] Ammar Waleed, 2018, NAACL 18, DOI [10.18653/v1/n18-3011, DOI 10.18653/V1/N18-3011]
  • [2] Beltagy I, 2019, 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019), P3615
  • [3] Bhatia P, 2020, Arxiv, DOI arXiv:2007.09186
  • [4] Biester Laura, 2022, P 2 WORKSH NLP POS I
  • [5] Brown TB, 2020, ADV NEUR IN, V33
  • [6] Cachola I, 2020, FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EMNLP 2020, P4766
  • [7] Campello Ricardo J. G. B., 2013, Advances in Knowledge Discovery and Data Mining. 17th Pacific-Asia Conference (PAKDD 2013). Proceedings, P160, DOI 10.1007/978-3-642-37456-2_14
  • [8] Chen Anthony, 2019, P 2 WORKSH MACH READ, P119, DOI [DOI 10.18653/V1/D19-5817, 10.18653/v1/D19-5817]
  • [9] Artificial Intelligence in Education: A Review
    Chen, Lijia
    Chen, Pingping
    Lin, Zhijian
    [J]. IEEE ACCESS, 2020, 8 (08): : 75264 - 75278
  • [10] A definition, benchmark and database of AI for social good initiatives
    Cowls, Josh
    Tsamados, Andreas
    Taddeo, Mariarosaria
    Floridi, Luciano
    [J]. NATURE MACHINE INTELLIGENCE, 2021, 3 (02) : 111 - 115