Joonghyuk Hahn


pdf bib
SuperST: Superficial Self-Training for Few-Shot Text Classification
Ju-Hyoung Lee | Joonghyuk Hahn | Hyeon-Tae Seo | Jiho Park | Yo-Sub Han
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

In few-shot text classification, self-training is a popular tool in semi-supervised learning (SSL). It relies on pseudo-labels to expand data, which has demonstrated success. However, these pseudo-labels contain potential noise and provoke a risk of underfitting the decision boundary. While the pseudo-labeled data can indeed be noisy, fully acquiring this flawed data can result in the accumulation of further noise and eventually impacting the model performance. Consequently, self-training presents a challenge: mitigating the accumulation of noise in the pseudo-labels. Confronting this challenge, we introduce superficial learning, inspired by pedagogy’s focus on essential knowledge. Superficial learning in pedagogy is a learning scheme that only learns the material ‘at some extent’, not fully understanding the material. This approach is usually avoided in education but counter-intuitively in our context, we employ superficial learning to acquire only the necessary context from noisy data, effectively avoiding the noise. This concept serves as the foundation for SuperST, our self-training framework. SuperST applies superficial learning to the noisy data and fine-tuning to the less noisy data, creating an efficient learning cycle that prevents overfitting to the noise and spans the decision boundary effectively. Notably, SuperST improves the classifier accuracy for few-shot text classification by 18.5% at most and 8% in average, compared with the state-of-the-art SSL baselines. We substantiate our claim through empirical experiments and decision boundary analysis.


pdf bib
GDA: Grammar-based Data Augmentation for Text Classification using Slot Information
Joonghyuk Hahn | Hyunjoon Cheon | Elizabeth Orwig | Su-Hyeon Kim | Sang-Ki Ko | Yo-Sub Han
Findings of the Association for Computational Linguistics: EMNLP 2023

Recent studies propose various data augmentation approaches to resolve the low-resource problem in natural language processing tasks. Data augmentation is a successful solution to this problem and recent strategies give variation on sentence structures to boost performance. However, these approaches can potentially lead to semantic errors and produce semantically noisy data due to the unregulated variation of sentence structures. In an effort to combat these semantic errors, we leverage slot information, the representation of the context of keywords from a sentence, and form a data augmentation strategy which we propose, called GDA. Our strategy employs algorithms that construct and manipulate rules of context-aware grammar, utilizing this slot information. The algorithms extract recurrent patterns by distinguishing words with slots and form the “rules of grammar”—a set of injective relations between a sentence’s semantics and its syntactical structure—to augment the dataset. The augmentation is done in an automated manner with the constructed rules and thus, GDA is explainable and reliable without any human intervention. We evaluate GDA with state-of-the-art data augmentation techniques, including those using pre-trained language models, and the result illustrates that GDA outperforms all other data augmentation methods by 19.38%. Extensive experiments show that GDA is an effective data augmentation strategy that incorporates word semantics for more accurate and diverse data.

pdf bib
ATHENA: Mathematical Reasoning with Thought Expansion
Jb. Kim | Hazel Kim | Joonghyuk Hahn | Yo-Sub Han
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Solving math word problems depends on how to articulate the problems, the lens through which models view human linguistic expressions. Real-world settings count on such a method even more due to the diverse practices of the same mathematical operations. Earlier works constrain available thinking processes by limited prediction strategies without considering their significance in acquiring mathematical knowledge. We introduce Attention-based THought Expansion Network Architecture (ATHENA) to tackle the challenges of real-world practices by mimicking human thought expansion mechanisms in the form of neural network propagation. A thought expansion recurrently generates the candidates carrying the thoughts of possible math expressions driven from the previous step and yields reasonable thoughts by selecting the valid pathways to the goal. Our experiments show that ATHENA achieves a new state-of-the-art stage toward the ideal model that is compelling in variant questions even when the informativeness in training examples is restricted.


pdf bib
Boosting Code Summarization by Embedding Code Structures
Jikyoeng Son | Joonghyuk Hahn | HyeonTae Seo | Yo-Sub Han
Proceedings of the 29th International Conference on Computational Linguistics

Recent research on code summarization relies on the structural information from the abstract syntax tree (AST) of source codes. It is, however, questionable whether it is the most effective to use AST for expressing the structural information. We find that a program dependency graph (PDG) can represent the structure of a code more effectively. We propose PDG Boosting Module (PBM) that encodes PDG into graph embedding and the framework to implement the proposed PBM with the existing models. PBM achieves improvements of 6.67% (BLEU) and 7.47% (ROUGE) on average. We then analyze the experimental results, and examine how PBM helps the training of baseline models and its performance robustness. For the validation of robustness, we measure the performance of an out-of-domain benchmark dataset, and confirm its robustness. In addition, we apply a new evaluation measure, SBERT score, to evaluate the semantic performance. The models implemented with PBM improve the performance of SBERT score. This implies that they generate summaries that are semantically more similar to the reference summary.


pdf bib
Self-Training using Rules of Grammar for Few-Shot NLU
Joonghyuk Hahn | Hyunjoon Cheon | Kyuyeol Han | Cheongjae Lee | Junseok Kim | Yo-Sub Han
Findings of the Association for Computational Linguistics: EMNLP 2021

We tackle the problem of self-training networks for NLU in low-resource environment—few labeled data and lots of unlabeled data. The effectiveness of self-training is a result of increasing the amount of training data while training. Yet it becomes less effective in low-resource settings due to unreliable labels predicted by the teacher model on unlabeled data. Rules of grammar, which describe the grammatical structure of data, have been used in NLU for better explainability. We propose to use rules of grammar in self-training as a more reliable pseudo-labeling mechanism, especially when there are few labeled data. We design an effective algorithm that constructs and expands rules of grammar without human involvement. Then we integrate the constructed rules as a pseudo-labeling mechanism into self-training. There are two possible scenarios regarding data distribution: it is unknown or known in prior to training. We empirically demonstrate that our approach substantially outperforms the state-of-the-art methods in three benchmark datasets for both scenarios.