Papers with text data augmentation
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange (D19-1)
Copied to clipboard
| Challenge: | Existing methods to adjust semantics of text while preserving its style have not been investigated to the best of our knowledge. |
| Approach: | They propose to use masking (replacement) rate threshold as an adjustable parameter to control the amount of semantic change in the text. |
| Outcome: | The proposed pipeline outperforms baseline models on Yelp reviews, Amazon reviews, and news headlines in terms of its Semantic Text Exchange Score (STES) |
Direct Metric Optimization for Image Captioning through Reward-Weighted Augmented Data Utilization (2024.acl-long)
Copied to clipboard
Takumi Takada, Yuma Suzuki, Hiroki Takushima, Hayato Tanoue, Haruki Sato, Aiswariya Kumar, Hiroki Nishihara, Takayuki Hori, Kazuya Ueki
| Challenge: | Recent large-scale vision language models (VLMs) lack continuity between learning objective and performance metrics. |
| Approach: | They propose a lightweight final-metric-optimizing training method that replaces the expensive exploration process in RL with an offline, diverse text data augmentation method. |
| Outcome: | The proposed method achieves comparable performance to state-of-the-art RL method while saving hundreds of times more model forwarding iterations and greater amounts of computation time. |
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for text data augmentation are limited to simple tasks and weak baselines. |
| Approach: | They propose a data augmentation method FlipDA that uses a generative model and a classifier to generate label-flipped data. |
| Outcome: | The proposed method improves many tasks while not negatively affecting the others. |
Automatic Gloss-level Data Augmentation for Sign Language Translation (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for enhancing sign language text data are insufficient . fewer studies have been performed on text data augmentation compared to video data . |
| Approach: | They propose three methods to augment sign language text data using Korean sign language gloss dictionary. |
| Outcome: | The proposed method improves translation performance by 0.204 and 0.170 compared to the original data. |