Tianchu Ji


2023

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Efficient Methods for Natural Language Processing: A Survey
Marcos Treviso | Ji-Ung Lee | Tianchu Ji | Betty van Aken | Qingqing Cao | Manuel R. Ciosici | Michael Hassid | Kenneth Heafield | Sara Hooker | Colin Raffel | Pedro H. Martins | André F. T. Martins | Jessica Zosa Forde | Peter Milder | Edwin Simpson | Noam Slonim | Jesse Dodge | Emma Strubell | Niranjan Balasubramanian | Leon Derczynski | Iryna Gurevych | Roy Schwartz
Transactions of the Association for Computational Linguistics, Volume 11

Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.

2021

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On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers
Tianchu Ji | Shraddhan Jain | Michael Ferdman | Peter Milder | H. Andrew Schwartz | Niranjan Balasubramanian
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021