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.
|Title of host publication||AAAI-22 Technical Tracks 10|
|Publisher||Association for the Advancement of Artificial Intelligence|
|Number of pages||9|
|ISBN (Electronic)||1577358767, 9781577358763|
|Publication status||Published - 2022 Jun 30|
|Event||36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online|
Duration: 2022 Feb 22 → 2022 Mar 1
|Name||Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022|
|Conference||36th AAAI Conference on Artificial Intelligence, AAAI 2022|
|Period||22/2/22 → 22/3/1|
Bibliographical noteFunding 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.
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence