Image and Text: Fighting the same Battle? Super Resolution Learning for Imbalanced Text Classification (2023)

Abstract: In this paper, we propose SRL4NLP, a new approach for data augmentation by drawing an analogy between image and text processing: Super-resolution learning. This method is based on using high-resolution images to overcome the problem of low resolution images. While this technique is a common usage in image processing when images have a low resolution or are too noisy, it has never been used in NLP. We therefore propose the first adaptation of this method for text classification and evaluate its effectiveness on urgency detection from tweets posted in crisis situations, a very challenging task where messages are scarce and highly imbalanced. We show that this strategy is efficient when compared to competitive state-of-the-art data augmentation techniques on several benchmarks datasets in two languages.

## introduction the performances of nlp classification tasks are largely influenced by the availability of good quality training data with sufficient number of representative instances. this is particularity salient for (highly) imbalanced text classification problems, like malware detection @xcite , relation extraction @xcite , hate speech detection @xcite as well as urgency detection @xcite . to augment minority classes, many data augmentation strategies have been proposed while ensuring models generalization. roughly, these strategies can operate either at the data or model level. databased approaches generate synthetic new instances relying on lexical/syntactic variations (paraphrasing, substitution, etc.) or pre-trained generative models (e.g., gpt2, gpt3) @xcite @xcite . modelbased approaches on the other hand fight class imbalance during training via (a) some dedicated loss functions by assigning higher wrong classification costs to classes with small proportion @xcite , (b) specific architectures such as multi-task learning leveraging auxiliary tasks @xcite and knowledge distillation that trains smaller model with soft labels @xcite , or (c) text interpolation that mixes up original and perturbed samples in the hidden space @xcite . while data-based techniques are easy to implement with low computational cost compared to model-based ones @xcite , current methods have difficulty to generate meaningful instances as augmented data are often too close to the original ones, or semantically different which may result in instances that do not make sense to humans @xcite . prompt-based augmentation alleviates these issues but at the price of training large-scale language models. in addition, they turn to be sensitive to prompt design in terms of the choice and order of the input examples @xcite . in this paper, we propose a new approach for data augmentation by drawing an analogy between image and text processing: super-resolution learning @xcite . this method is based on using high-resolution images to overcome the problem of low resolution images and has been successfully employed in image processing when images have a low resolution or are too noisy @xcite @xcite . it has however never been adapted to nlp classification tasks. in this context, we make a parallel between image and text by considering noisy and short user generated content (e.g., social media posts) as a low resolution image and long well written documents (e.g., news papers, wikipedia, scientific articles) as a high resolution image which enables augmentation with more diverse and high quality data with less cost. mixed data from both types of resolution are then used to train supervised classifiers. we measure the effectiveness of super-resolution data augmentation on single label text classification tasks which: (i) show a step-imbalanced distribution with a high fraction of minority classes @xcite , (ii) are characterized by a long-tailed distribution with many instances for a small number of classes, but few for the remaining classes @xcite , (iii) involve low-quality instances (e.g., few tokens, ill-formed with grammar spelling/syntactic issues), and (iv) are about media events such as disasters (e.g., flood, earthquakes), health (e.g., vaccines, cancer, suicide) and sport, that are more likely to be discussed in factual external sources of information, adding therefore meaningful context. among nlp applications meeting these criteria (e.g., cybersecurity @xcite , medecine @xcite ), we experiment with urgency detection from tweets @xcite , a very challenging task aiming at classifying messages posted during crisis situations according to fine-grained intention to act categories (such as human/infrastructure damages, warning/advice). in this task, messages that require an urgent and rapid action from rescue teams are scarce and highly imbalanced compared to non-urgent ones @xcite , making it a very interesting case study for super-resolution learning. in this paper, our contributions are: in the following, section 2 presents the original machine-learned super-resolution method and how we adapted it for nlp. section 3 presents the datasets and section 4 presents the experimental settings used in this study. section 5 presents the results. we compare our approach to related work in section 6. finally, we conclude drawing some perspectives for future work. ## datasets the literature on emergency detection has been growing fast in the recent years and several datasets (mainly tweets) have been proposed to account for crisis-related phenomena. foot_0 messages are annotated according to relevant categories that are deemed to fit the information needs of various stakeholders like humanitarian organizations, local police and firefighters. well-known dimensions include relatedness to identify whether the message content is useful @xcite and situation awareness (also known as urgency) to filter out on-topic relevant vs. on-topic irrelevant information @xcite @xcite . we evaluate several data augmentation strategies on different datasets in english and french since datasets for crisis management are available in these languages (see ## conclusion by drawing an analogy between image and text processing, we have proposed srl4nlp, a new approach based on super-resolution learning to augment low-resolution imbalanced training data (for example, tweets) with similar texts having a higher resolution (paragraphs). we have evaluated its effectiveness on urgency detection from tweets posted in crisis situations, relying on imbalanced benchmark datasets in both english and french. we have compared the classification performances over these datasets augmented with few data obtained with several data augmentation methods. the results show statistically significant f-scores in out-of-type evaluation setting, outperforming existing data-based and model-based augmentation approaches. srl4nlp has also a very low computational cost since it avoids the generation of artificial data and their manual annotation. for future work, we plan to rely on other external sources for augmenting our data (e.g., news, government reports, return of operating experiences) as well as experiment with other use cases (see below).