@inproceedings{bajpai-etal-2024-fair,
title = "{FAIR}: Filtering of Automatically Induced Rules",
author = "Bajpai, Divya Jyoti and
Maheshwari, Ayush and
Hanawal, Manjesh and
Ramakrishnan, Ganesh",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.34",
pages = "573--588",
abstract = "Availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains. Weak supervision offers a promising alternative by accelerating the creation of labeled training data using domain-specific rules. However, it requires users to write a diverse set of high-quality rules to assign labels to the unlabeled data (eg., Snorkel (CITATION)). Automatic Rule Induction (ARI) approaches such as Snuba (CITATION) circumvent this problem by automatically creating rules from features on a small labeled set and filtering a final set of rules from them. In the ARI approach, the crucial step is to filter out a set of a high-quality useful subset of rules from the large set of automatically created rules. In this paper, we propose an algorithm FAIR (Filtering of Automatically Induced Rules) to filter rules from a large number of automatically induced rules using submodular objective functions that account for the collective precision, coverage, and conflicts of the rule set. We experiment with three ARI approaches and five text classification datasets to validate the superior performance of our algorithm with respect to several semi-supervised label aggregation approaches. We show that our approach achieves statistically significant results in comparison to existing rule-filtering approaches. The anonymized source code is available at \url{https://anonymous.4open.science/r/FAIR-LF-Induction-9B60}.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bajpai-etal-2024-fair">
<titleInfo>
<title>FAIR: Filtering of Automatically Induced Rules</title>
</titleInfo>
<name type="personal">
<namePart type="given">Divya</namePart>
<namePart type="given">Jyoti</namePart>
<namePart type="family">Bajpai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ayush</namePart>
<namePart type="family">Maheshwari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manjesh</namePart>
<namePart type="family">Hanawal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ganesh</namePart>
<namePart type="family">Ramakrishnan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yvette</namePart>
<namePart type="family">Graham</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Matthew</namePart>
<namePart type="family">Purver</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">St. Julian’s, Malta</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains. Weak supervision offers a promising alternative by accelerating the creation of labeled training data using domain-specific rules. However, it requires users to write a diverse set of high-quality rules to assign labels to the unlabeled data (eg., Snorkel (CITATION)). Automatic Rule Induction (ARI) approaches such as Snuba (CITATION) circumvent this problem by automatically creating rules from features on a small labeled set and filtering a final set of rules from them. In the ARI approach, the crucial step is to filter out a set of a high-quality useful subset of rules from the large set of automatically created rules. In this paper, we propose an algorithm FAIR (Filtering of Automatically Induced Rules) to filter rules from a large number of automatically induced rules using submodular objective functions that account for the collective precision, coverage, and conflicts of the rule set. We experiment with three ARI approaches and five text classification datasets to validate the superior performance of our algorithm with respect to several semi-supervised label aggregation approaches. We show that our approach achieves statistically significant results in comparison to existing rule-filtering approaches. The anonymized source code is available at https://anonymous.4open.science/r/FAIR-LF-Induction-9B60.</abstract>
<identifier type="citekey">bajpai-etal-2024-fair</identifier>
<location>
<url>https://aclanthology.org/2024.eacl-long.34</url>
</location>
<part>
<date>2024-03</date>
<extent unit="page">
<start>573</start>
<end>588</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T FAIR: Filtering of Automatically Induced Rules
%A Bajpai, Divya Jyoti
%A Maheshwari, Ayush
%A Hanawal, Manjesh
%A Ramakrishnan, Ganesh
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F bajpai-etal-2024-fair
%X Availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains. Weak supervision offers a promising alternative by accelerating the creation of labeled training data using domain-specific rules. However, it requires users to write a diverse set of high-quality rules to assign labels to the unlabeled data (eg., Snorkel (CITATION)). Automatic Rule Induction (ARI) approaches such as Snuba (CITATION) circumvent this problem by automatically creating rules from features on a small labeled set and filtering a final set of rules from them. In the ARI approach, the crucial step is to filter out a set of a high-quality useful subset of rules from the large set of automatically created rules. In this paper, we propose an algorithm FAIR (Filtering of Automatically Induced Rules) to filter rules from a large number of automatically induced rules using submodular objective functions that account for the collective precision, coverage, and conflicts of the rule set. We experiment with three ARI approaches and five text classification datasets to validate the superior performance of our algorithm with respect to several semi-supervised label aggregation approaches. We show that our approach achieves statistically significant results in comparison to existing rule-filtering approaches. The anonymized source code is available at https://anonymous.4open.science/r/FAIR-LF-Induction-9B60.
%U https://aclanthology.org/2024.eacl-long.34
%P 573-588
Markdown (Informal)
[FAIR: Filtering of Automatically Induced Rules](https://aclanthology.org/2024.eacl-long.34) (Bajpai et al., EACL 2024)
ACL
- Divya Jyoti Bajpai, Ayush Maheshwari, Manjesh Hanawal, and Ganesh Ramakrishnan. 2024. FAIR: Filtering of Automatically Induced Rules. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 573–588, St. Julian’s, Malta. Association for Computational Linguistics.