Data Boost: Text Data Augmentation through Reinforcement Learning Guided Conditional Generation

By Ruibo Liu, Guangxuan Xu, Chenyan Jia, Weicheng Ma, Lili Wang, Soroush Vosoughi. 2020

115 citations recorded by the source corpus.

Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which augments data through reinforcement learning guided conditional generation. We evaluate Data Boost on three diverse text classification tasks under five different classifier architectures. The result shows that Data Boost can boost the performance of classifiers especially in low-resource data scenarios. For instance, Data Boost improves F1 for the three tasks by 8.7% on average when given only 10% of the whole data for training. We also compare Data Boost with six prior text augmentation methods. Through human evaluations (N=178), we confirm that Data Boost augmentation has comparable quality as the original data with respect to readability and class consistency.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, background): “…et al., 2021), augmenting misclassiﬁed examples (Dreossi et al., 2018), BERT cross-encoder labeling of new inputs (Thakur et al., 2021), guided generation us-ing large-scale generative language models (Liu et al., 2020b,c), and automated text augmentation (Hu et al., 2019; Cai et al., 2020).”
FUDGE: Controlled Text Generation With Future Discriminators (2021, background): “A second type of approach instead conditions on a desired attribute by backpropagating gradients, either to directly modify model activations (Dathathri et al., 2019; Liu et al., 2020) or to find a trigger”
A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models (2022, methodology): “Adapted Module [72, 112, 156] Prompt [54, 63, 103, 124] [10, 68, 152, 158] Reinforce Learning [79, 94, 130, 132, 171] Instruction Tuning [94] Refact/Retrain computationally expensive training; higher text quality; better controllability Retrain [58],[163] Refact [14, 45, 143, 157, 163, 167]…”
Mitigating Political Bias in Language Models Through Reinforced Calibration (2021, background): “Other augmentation techniques have reduced gender bias in hate speech detection (Park, Shin, and Fung 2018; Liu et al. 2020), knowledge graph building (Mitchell et al.”
Enhanced Offensive Language Detection Through Data Augmentation (2020, methodology): “Our method does not require external datasets or rely on training auxiliary tools; instead, we make use of an off-the-shelf language model (LM) and focus on mining the potentially informative features for the low-resource data classiﬁcation (Liu et al. 2020).”
Substructure Substitution: Structured Data Augmentation for NLP (2021, methodology): “…has explored generating new examples by training a conditional text generation model (Bergmanis et al., 2017; Liu et al., 2020a; Ding et al., 2020; Liu et al., 2020b, inter alia ), or applying post-processing on the examples generated by pretrained models (Yang et al., 2020; Wan et al., 2020; Yoo…”