Neural Architecture Search for Extreme Multi-label Text Classification

Loïc Pauletto*, Massih-Reza Amini, Rohit Babbar, Nicolas Winckler

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Extreme classification and Neural Architecture Search (NAS) are research topics which have recently gained a lot of interest. While the former has been mainly motivated and applied in e-commerce and Natural Language Processing (NLP) applications, the NAS approach has been applied to a small variety of tasks, mainly in image processing. In this study, we extend the scope of NAS to the task of extreme multilabel classification (XMC). We propose a neuro-evolution approach, which was found to be the most suitable for a variety of tasks. Our NAS method automatically finds architectures that give competitive results with respect to the state of the art (and superior to other methods) with faster convergence. In addition, we perform analysis of the weights of the architecture blocks to provide insight into the importance of different operations that have been selected by the method.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing
Subtitle of host publicationICONIP 2020: Neural Information Processing
Number of pages12
ISBN (Electronic)978-3-030-63836-8
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Neural Information Processing - Virtual, Online
Duration: 18 Nov 202022 Nov 2020

Publication series

NameLecture Notes in Computer Science


ConferenceInternational Conference on Neural Information Processing
Abbreviated titleICONIP
CityVirtual, Online


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