We propose a three-level optimization framework to perform text augmentation and the downstream task end-to- end.
Apr 6, 2022 · We propose a three-level optimization framework to perform text augmentation and the downstream task end-to-end.
Our framework consists of three learning stages. A text summarization model is trained to perform data augmentation at the first stage. Each summarization ...
TACL paper- "A Multi-Level Optimization Framework for End-to-End Text Augmentation" code. This repository is the code for end-to-end data augmentation.
To address this problem, we propose a three-level optimization framework to perform text augmentation and the downstream task end-to- end. The augmentation ...
Multilevel optimization has been widely adopted as a mathematical foundation for a myriad of machine learning problems, such as hyperparameter optimization, ...
The multi-level optimization in Deep-learning can be viewed as a solution to Auto-AI, where the unknown parameters are optimized subject to some constraints.
The MMDA architecture attempts to eliminate the need for an external LM, by enabling seamless mixing of large text datasets with significantly smaller ...
Jul 29, 2024 · In this paper, we introduce QAEA-DR, a novel unified text augmentation framework for dense retrieval. This approach optimizes the original text ...
Nov 21, 2023 · This paper proposes a novel approach to text augmentation that is designed to balance the diversity and semantic fidelity of augmented text. The ...