Papers with neural language generation models
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation (2020.tacl-1)
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| Challenge: | Existing models for story generation suffer from repetition, logic conflicts, and lack of long-range coherence . |
| Approach: | They propose to utilize commonsense knowledge from external knowledge bases to generate reasonable stories by multi-task learning. |
| Outcome: | The proposed model can generate more reasonable stories than state-of-the-art models, compared with existing models, showing that it can capture useful semantic and syntactic features. |
A Natural Bias for Language Generation Models (2023.acl-short)
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| Challenge: | a standard probabilistic model for language generation has likely not yet learnt many semantic or syntactic rules of natural language, making it difficult to estimate the probability distribution over next tokens. |
| Approach: | They propose to initialise bias terms in a model's final linear layer with the log-unigram distribution and use it to output the unigram frequency statistics as prior knowledge. |
| Outcome: | The proposed method improves learning efficiency and improves overall performance. |