Learning to make effective arguments is vital for the development of critical-thinking in students and, hence, for their academic and career success. Detecting argument components is crucial for developing systems that assess students’ ability to develop arguments. Traditionally, supervised learning has been used for this task, but this requires a large corpus of reliable training examples which are often impractical to obtain for student writing. Large language models have also been shown to be effective few-shot learners, making them suitable for low-resource argument detection. However, concerns such as latency, service reliability, and data privacy might hinder their practical applicability. To address these challenges, we present a low-resource classification approach that combines the intrinsic entailment relationship among the argument elements with a parameter-efficient prompt-tuning strategy. Experimental results demonstrate the effectiveness of our method in reducing the data and computation requirements of training an argument detection model without compromising the prediction accuracy. This suggests the practical applicability of our model across a variety of real-world settings, facilitating broader access to argument classification for researchers spanning various domains and problem scenarios.

Certain types of classification problems may be performed at multiple levels of granularity; for example, we might want to know the sentiment polarity of a document or a sentence, or a phrase. Often, the prediction at a greater-context (e.g., sentences or paragraphs) may be informative for a more localized prediction at a smaller semantic unit (e.g., words or phrases). However, directly inferring the most salient local features from the global prediction may overlook the semantics of this relationship. This work argues that inference along the contraposition relationship of the local prediction and the corresponding global prediction makes an inference framework that is more accurate and robust to noise. We show how this contraposition framework can be implemented as a transfer function that rewrites a greater-context from one class to another and demonstrate how an appropriate transfer function can be trained from a noisy user-generated corpus. The experimental results validate our insight that the proposed contrapositive framework outperforms the alternative approaches on resource-constrained problem domains.

Data augmentation has been shown to be effective in providing more training data for machine learning and resulting in more robust classifiers. However, for some problems, there may be multiple augmentation heuristics, and the choices of which one to use may significantly impact the success of the training. In this work, we propose a metric for evaluating augmentation heuristics; specifically, we quantify the extent to which an example is “hard to distinguish” by considering the difference between the distribution of the augmented samples of different classes. Experimenting with multiple heuristics in two prediction tasks (positive/negative sentiment and verbosity/conciseness) validates our claims by revealing the connection between the distribution difference of different classes and the classification accuracy.

Pleonasms are words that are redundant. To aid the development of systems that detect pleonasms in text, we introduce an annotated corpus of semantic pleonasms. We validate the integrity of the corpus with interannotator agreement analyses. We also compare it against alternative resources in terms of their effects on several automatic redundancy detection methods.