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

• URL: http://arxiv.org/abs/2112.11916v1
• Date: Thu, 16 Dec 2021 09:56:35 GMT
• Title: ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification
• Authors: Hazel Kim, Daecheol Woo, Seong Joon Oh, Jeong-Won Cha, Yo-Sub Han
• Abstract summary: We present the data augmentation using Lexicalized Probabilistic context-free grammars (ALP) Experiments on few-shot text classification tasks demonstrate that ALP enhances many state-of-the-art classification methods. We argue empirically that the traditional splitting of training and validation sets is sub-optimal compared to our novel augmentation-based splitting strategies.
• Score: 11.742065170002162
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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