Image and Text: Fighting the same Battle? Super Resolution Learning for Imbalanced Text Classification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Using high-resolution images to overcome the problem of low resolution has never been used in NLP. |
| Approach: | They propose a super-resolution learning method that uses high-res images to overcome the problem of low resolution images. |
| Outcome: | The proposed method is efficient when compared to state-of-the-art methods on several benchmarks datasets in two languages. |
Similar Papers
Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
Copied to clipboard
| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
| Approach: | This tutorial introduces contrastive learning and provides an introduction to the techniques. |
| Outcome: | This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them. |
On the (In)Effectiveness of Images for Text Classification (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing studies have focused on text classification, but have shown that images do not improve NLP tasks. |
| Approach: | They focus on text classification, where images complement the text and the Wikipedia page can be in one of a number of different languages. |
| Outcome: | The proposed model trains without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing. |
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning (2021.naacl-main)
Copied to clipboard
| Challenge: | a few-shot text classification task requires a large number of output classes, with few training examples per class. |
| Approach: | They propose a data augmentation technique suitable for training with limited data for few-shot, highly-multiclass text classification scenarios. |
| Outcome: | The proposed technique improves performance on four classification tasks by 3.0% on average. |
Learning with Limited Text Data (2022.acl-tutorials)
Copied to clipboard
| Challenge: | Natural Language Processing (NLP) relies on labeled data to perform state-of-the-art performance . labeles are often required to label large amounts of textual data . this tutorial will provide an overview of labeleing in NLP . |
| Approach: | This tutorial will provide a systematic overview of methods for learning from limited labeled data. |
| Outcome: | This tutorial will provide a systematic and up-to-date overview of the proposed methods . it will highlight current challenges and future directions . |
Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification (2022.acl-short)
Copied to clipboard
| Challenge: | Existing methods for multi-label text classification neglect the knowledge from the existing similar instances when predicting labels of a specific text. |
| Approach: | They propose a k nearest neighbor mechanism which retrieves several neighbor instances and interpolates the model output with their labels. |
| Outcome: | Extensive experiments show that the proposed method can bring significant performance improvements to multiple MLTC models including state-of-the-art pretrained and non-pretrained ones. |
Constructing contrastive samples via summarization for text classification with limited annotations (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Various contrastive learning methods have been developed and lead to state-of-the-art performance in many computer vision tasks. |
| Approach: | They propose a method to construct efficient contrastive samples using text summarization to gain better representations of text classification tasks with limited annotations. |
| Outcome: | The proposed framework gains better representations on text classification tasks with limited annotations and is compared with existing methods on real-world text classification datasets. |
A Strong and Robust Baseline for Text-Image Matching (P19-2)
Copied to clipboard
| Challenge: | Text-image matching is one of the most popular methods for training text-image embeddings. |
| Approach: | They propose to use a kNN-margin loss that utilizes hard negatives and is robust to noise . they advocate using Inverted Softmax and Cross-modal Local Scaling during inference . |
| Outcome: | The proposed loss function is robust to noise and pseudo negatives are tolerable . the proposed loss functions improve scores of all metrics by a large margin . |
Text Augmentation Using Dataset Reconstruction for Low-Resource Classification (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for text classification use labeled data, but labeles are expensive and difficult to obtain. |
| Approach: | They propose a novel method of data augmentation using the text-generation capabilities of language models. |
| Outcome: | The proposed method improves the current state-of-the-art methods for data augmentation on multi-class datasets. |
A Survey of Data Augmentation Approaches for NLP (2021.findings-acl)
Copied to clipboard
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard Hovy
| Challenge: | Data augmentation is a field of research that has been underexplored due to the discrete nature of language data. |
| Approach: | They present a comprehensive survey of data augmentation for NLP by summarizing the literature in a structured manner. |
| Outcome: | The proposed methods are used for popular NLP applications and tasks and highlight current challenges and directions for future research. |
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems (N19-1)
Copied to clipboard
Steffen Eger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych
| Challenge: | Recent studies show that visual similarity can play a decisive role in assessing the meaning of characters. |
| Approach: | They investigate the impact of visual adversarial attacks on current NLP systems . they explore three shielding methods that significantly improve the robustness of the models . |
| Outcome: | The proposed methods improve performance but still fall behind non-attack scenarios. |