Bridging the Gap between Pre-Training and Fine-Tuning for Commonsense Generation (2023.findings-eacl)
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| Challenge: | Existing methods focusing on this task usually concatenate the concatened concepts words as the inputs of a pre-trained language model (PLM) however, in pre-training, the input is often corrupted sentences with correct word order. |
| Approach: | They propose a two-stage framework to improve the ability of pre-trained language models to deal with masked sentences with incorrect word order and a special token to make the input distribution more similar to the one used in pre-training. |
| Outcome: | The proposed method is able to generate a sentence containing all given concepts and correctly describe the relations between concepts. |
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