@inproceedings{duh-zhang-2023-automl,
title = "{A}uto{ML} for {NLP}",
author = "Duh, Kevin and
Zhang, Xuan",
editor = "Zanzotto, Fabio Massimo and
Pradhan, Sameer",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-tutorials.5/",
doi = "10.18653/v1/2023.eacl-tutorials.5",
pages = "25--26",
abstract = "Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. As an umbrella term that includes topics like hyperparameter optimization and neural architecture search, AutoML has recently become mainstream at major conferences such as NeurIPS, ICML, and ICLR. What does this mean to NLP? Currently, models are often built in an ad hoc process: we might borrow default hyperparameters from previous work and try a few variant architectures, but it is never guaranteed that final trained model is optimal. Automation can introduce rigor in this model-building process. This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="duh-zhang-2023-automl">
<titleInfo>
<title>AutoML for NLP</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kevin</namePart>
<namePart type="family">Duh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xuan</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Fabio</namePart>
<namePart type="given">Massimo</namePart>
<namePart type="family">Zanzotto</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sameer</namePart>
<namePart type="family">Pradhan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Dubrovnik, Croatia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. As an umbrella term that includes topics like hyperparameter optimization and neural architecture search, AutoML has recently become mainstream at major conferences such as NeurIPS, ICML, and ICLR. What does this mean to NLP? Currently, models are often built in an ad hoc process: we might borrow default hyperparameters from previous work and try a few variant architectures, but it is never guaranteed that final trained model is optimal. Automation can introduce rigor in this model-building process. This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process.</abstract>
<identifier type="citekey">duh-zhang-2023-automl</identifier>
<identifier type="doi">10.18653/v1/2023.eacl-tutorials.5</identifier>
<location>
<url>https://aclanthology.org/2023.eacl-tutorials.5/</url>
</location>
<part>
<date>2023-05</date>
<extent unit="page">
<start>25</start>
<end>26</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T AutoML for NLP
%A Duh, Kevin
%A Zhang, Xuan
%Y Zanzotto, Fabio Massimo
%Y Pradhan, Sameer
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F duh-zhang-2023-automl
%X Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. As an umbrella term that includes topics like hyperparameter optimization and neural architecture search, AutoML has recently become mainstream at major conferences such as NeurIPS, ICML, and ICLR. What does this mean to NLP? Currently, models are often built in an ad hoc process: we might borrow default hyperparameters from previous work and try a few variant architectures, but it is never guaranteed that final trained model is optimal. Automation can introduce rigor in this model-building process. This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process.
%R 10.18653/v1/2023.eacl-tutorials.5
%U https://aclanthology.org/2023.eacl-tutorials.5/
%U https://doi.org/10.18653/v1/2023.eacl-tutorials.5
%P 25-26
Markdown (Informal)
[AutoML for NLP](https://aclanthology.org/2023.eacl-tutorials.5/) (Duh & Zhang, EACL 2023)
ACL
- Kevin Duh and Xuan Zhang. 2023. AutoML for NLP. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, pages 25–26, Dubrovnik, Croatia. Association for Computational Linguistics.