CHEF in the Language Kitchen: A Generative Data Augmentation Leveraging K orean Morpheme Ingredients (2023)

Abstract: Korean morphological variations present unique opportunities and challenges in natural language processing (NLP), necessitating an advanced understanding of morpheme-based sentence construction. The complexity of morphological variations allows for diverse sentence forms based on the syntactic-semantic integration of functional morphemes (i.e., affixes) to lexical morphemes (i.e., roots). With this in mind, we propose a method - CHEF, replicating the morphological transformations inherent in sentences based on lexical and functional morpheme combinations through generative data augmentation. CHEF operates using a morpheme blender and a label discriminator, thereby enhancing the diversity of Korean sentence forms by capturing the properties of agglutination while maintaining label consistency. We conduct experiments on Korean multiple classification datasets, improving model performance in full- and few-shot settings. Our proposed method boosts performance beyond the preceding data augmentation methods without incurring external data usage. We demonstrate that our approach achieves comparable results yielded by augmentation techniques that use large language models (LLMs).

## introduction as an agglutinative language, korean encompasses a rich array of functional morphemes @xcite . deep learning-based research in korean nlp aims to enhance linguistic construction efficiency through morpheme segmentation. previous studies have explored morphological analysis to improve model performance @xcite @xcite . the korean language presents distinct challenges and opportunities with respect to transformations contingent upon the combinatory patterns between lexical and functional morphemes @xcite . ## related work data augmentation is widely recognized in deep learning research as a valuable approach to addressing the scarcity of annotated data. it aids in ensuring that the distribution of the training data maintains robustness even in unseen tests. over time, several methods have been developed to augment textual content, expanding the diversity and quantity of available training data. @xcite proposed a method of word substitution using a synonym thesaurus based on rules, which is followed by augmentation methods utilizing a thesaurus in the studies of @xcite . @xcite suggested an easy data augmentation (eda) method using word-level replacement, random insertion, random deletion, and random swap. @xcite introduced an easier data augmentation (aeda) technique, improving text classification performance by randomly inserting six pre-defined punctuation marks. another strategy involves mixup-based data augmentation @xcite , combining two sample data to create new training data. mixup augmentation was initially applied to image data and extended to text-based deep neural networks @xcite . subsequently, it has also been applied to textual data augmentation using transformer-based methods, serving as a technique to mitigate model overfitting and enhance generalization capabilities @xcite @xcite . @xcite proposed a back translation method for augmenting data by performing a round-trip translation on data written in the original language, using a neural machine translation system trained on human-annotated data. data augmentation based on back translation has evolved as one of the crucial techniques since it allows for rewriting the entire sentence, rather than just wordlevel alterations @xcite @xcite . recently, research on data augmentation using pre-trained language models has been actively pursued @xcite @xcite and schütze, 2021; @xcite . among them, the data augmentation technique through counterfactual is particularly noteworthy, demonstrating improvements to the stateof-the-art performance levels across various benchmark datasets @xcite @xcite . with the advent of llms, considerable progress has been made in overcoming augmentation constraints through generative ai @xcite . thus, we proceed assuming that it is feasible to secure textual content with llms. nevertheless, it is still necessary to expend financial and temporal resources to identify the optimal direction for augmentation. as a result, we focus on determining which augmentation method could benefit model learning from korean data. given the diverse derivational and inflectional forms inherent in the agglutinative korean language, we propose chef -an augmentation method that considers these linguistic properties. ## chef taking inspiration from commongen (lin et al., 2020) and korean commongen @xcite , we focus on the data-to-text generation capabilities of sequence-to-sequence models. common-gen and korean commongen necessitate generative commonsense reasoning based on given concept sets, where given concept sets are used to determine the feasibility of constructing plausible sentences. both conditional generation tasks are to make generative language models learn a function f : @xmath0, ..., c n } to make a target sentence t based on the relation within c. we perceive a parallel between this process and combining ingredients to create a complete dish. in particular, @xcite have shown that combining lexical morphemes yields optimal parsing for generating korean sentences. consequently, we concentrate on harnessing the sentence augmentation effect of sequence-to-sequence models by assembling ingredients of morpheme sets. ## experimental settings we introduce experimental settings used for the experiments. more details are in appendix a ## experimental results in this section, we evaluate a set of multiple classification datasets with our proposed method. ## conclusion we introduce chef, a novel data augmentation method designed for the korean language, which is inherently agglutinative and rich in morphological variations. chef leverages the combinatory properties of lexical and functional morphemes in korean to construct linguistically diverse and labelconsistent synthetic data. by incorporating a morpheme blender and a label discriminator module, chef ensures that the generated synthetic data preserves the label information of the original dataset while introducing new linguistic forms through morphological alterations. our experiments demonstrate the effectiveness and robustness of chef across various scenarios. in future work, we plan to explore the adaptability of the chef architecture to other morphologically rich languages and further optimize the interaction between the morpheme blender and label discriminator.