Papers with task-specific techniques
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction (2020.findings-emnlp)
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| Challenge: | Existing approaches for grammatical error correction (GEC) rely on supervised learning with manually created datasets. |
| Approach: | They propose to denoise GEC datasets by leveraging prediction consistency of existing models. |
| Outcome: | The proposed method outperforms baseline methods on CoNLL-2014, JFLEG, and BEA-2019 benchmarks. |
PolyLM: Learning about Polysemy through Language Modeling (2021.eacl-main)
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| Challenge: | Existing methods to embed word senses have been overtaken by contextualized embeddings . alan ansell and jim koenig present a method which can be applied to downstream tasks . |
| Approach: | They propose a method which formulates learning sense embeddings as a language modeling problem. |
| Outcome: | The proposed method performs better than existing sense embedding methods on WSI tasks . it matches the current state-of-the-art specialized WSi method despite having six times fewer parameters . |
Denoising based Sequence-to-Sequence Pre-training for Text Generation (D19-1)
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| Challenge: | PoDA pre-trains encoders and decoders by denoising noise-corrupted text . Unlike encoder-only or decode-only methods, it can be used for text generation tasks without using any task-specific techniques. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) pre-training method PoDA which denoises autoencoders by denoising noise-corrupted text. |
| Outcome: | The proposed method improves model performance over strong baselines without using any task-specific techniques and significantly speed up convergence. |