Handling noisy labels for robustly learning from self-training data for low-resource sequence labeling

Debjit Paul, Mittul Singh, Michael A. Hedderich, Dietrich Klakow

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

1 Citation (Scopus)
14 Downloads (Pure)

Abstract

In this paper, we address the problem of effectively self-training neural networks in a lowresource setting. Self-training is frequently used to automatically increase the amount of training data. However, in a low-resource scenario, it is less effective due to unreliable annotations created using self-labeling of unlabeled data. We propose to combine self-training with noise handling on the self-labeled data. Directly estimating noise on the combined clean training set and self-labeled data can lead to corruption of the clean data and hence, performs worse. Thus, we propose the Clean and Noisy Label Neural Network which trains on clean and noisy self-labeled data simultaneously by explicitly modelling clean and noisy labels separately. In our experiments on Chunking and NER, this approach performs more robustly than the baselines. Complementary to this explicit approach, noise can also be handled implicitly with the help of an auxiliary learning task. To such a complementary approach, our method is more beneficial than other baseline methods and together provides the best performance overall.

Original languageEnglish
Title of host publicationNAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies - Proceedings of the Student Research Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages29-34
Number of pages6
ISBN (Electronic)9781950737154
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventConference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies - Minneapolis, United States
Duration: 3 Jun 20195 Jun 2019

Conference

ConferenceConference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies
Abbreviated titleNAACL HLT
CountryUnited States
CityMinneapolis
Period03/06/201905/06/2019

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