PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation (2023.eacl-main)
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| Challenge: | Curriculum Data Augmentation (CDA) presents synthetic data with increasing difficulties to neural models. |
| Approach: | They propose a curriculum-aware paraphrase generation module with bottom-k sampling and cyclic learning strategy that passes through the curriculums multiple times. |
| Outcome: | The proposed framework surpasses competitive baselines on few-shot text classification and dialogue generation. |
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Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
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| Challenge: | a number of dialog systems have been developed to perform tasks with high accuracy on benchmarks, but there is a problem with annotated seed data. |
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| Challenge: | Existing studies show that curriculum learning improves translation quality on machine translation . paraphrase generation allows a certain level of semantic divergence between source and target . |
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Generic and Trend-aware Curriculum Learning for Relation Extraction (2022.naacl-main)
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| Challenge: | Existing curriculum learning approaches for relation extraction are lacking in text graphs. |
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In-sample Curriculum Learning by Sequence Completion for Natural Language Generation (2023.acl-long)
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| Challenge: | Existing work on curriculum learning rely on task-specific expertise and cannot generalize to different tasks. |
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Learning to Selectively Learn for Weakly-supervised Paraphrase Generation (2021.emnlp-main)
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| Challenge: | Existing approaches to generate paraphrases with weak supervision are limited in real-world scenarios due to the lack of coherent and controllable generated paraphrase. |
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Paraphrase Augmented Task-Oriented Dialog Generation (2020.acl-main)
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| Challenge: | Neural generative models can perform dialog generation tasks with a large data set, but lack of high-quality data and expensive data annotation process limit their application in real world settings. |
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