ALP: Data Augmentation Using Lexicalized PCFGs for Few-Shot Text Classification

Hazel H. Kim, Woo Daecheol, Seong Joon Oh, Jeong Won Cha, Yo Sub Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Data augmentation has been an important ingredient for boosting performances of learned models. Prior data augmentation methods for few-shot text classification have led to great performance boosts. However, they have not been designed to capture the intricate compositional structure of natural language. As a result, they fail to generate samples with plausible and diverse sentence structures. Motivated by this, we present the data Augmentation using Lexicalized Probabilistic context-free grammars (ALP) that generates augmented samples with diverse syntactic structures with plausible grammar. The lexicalized PCFG parse trees consider both the constituents and dependencies to produce a syntactic frame that maximizes a variety of word choices in a syntactically preservable manner without specific domain experts. Experiments on few-shot text classification tasks demonstrate that ALP enhances many state-of-the-art classification methods. As a second contribution, we delve into the train-val splitting methodologies when a data augmentation method comes into play. We argue empirically that the traditional splitting of training and validation sets is sub-optimal compared to our novel augmentation-based splitting strategies that further expand the training split with the same number of labeled data. Taken together, our contributions on the data augmentation strategies yield a strong training recipe for few-shot text classification tasks.

Original languageEnglish
Title of host publicationAAAI-22 Technical Tracks 10
PublisherAssociation for the Advancement of Artificial Intelligence
Number of pages9
ISBN (Electronic)1577358767, 9781577358763
Publication statusPublished - 2022 Jun 30
Event36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online
Duration: 2022 Feb 222022 Mar 1

Publication series

NameProceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022


Conference36th AAAI Conference on Artificial Intelligence, AAAI 2022
CityVirtual, Online

Bibliographical note

Funding Information:
This research was supported by the NRF grant (NRF-2020R1A4A3079947), IITP grant (No. 2021-0-00354) and the AI Graduate School Program (No. 2020-0-01361) funded by the Korea government (MSIT). Han is a corresponding author.

Publisher Copyright:
Copyright © 2022, Association for the Advancement of Artificial Intelligence ( All rights reserved.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


Dive into the research topics of 'ALP: Data Augmentation Using Lexicalized PCFGs for Few-Shot Text Classification'. Together they form a unique fingerprint.

Cite this