Papers with BART-base
Conditional set generation using Seq2seq models (2022.emnlp-main)
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| Challenge: | Several NLP tasks are instances of set generation. |
| Approach: | They propose a model-independent data augmentation approach that enlarges the model with the signals of order-invariance and cardinality. |
| Outcome: | The proposed method improves performance on four benchmark datasets with no additional annotations. |
Inverse Reinforcement Learning for Text Summarization (2023.findings-emnlp)
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| Challenge: | Existing studies show that inverse reinforcement learning (RL) training has certain disadvantages such as object mismatch and exposure bias. |
| Approach: | They propose inverse reinforcement learning (IRL) as an effective paradigm for training abstractive summarization models. |
| Outcome: | The proposed model outperforms MLE and RL baselines on ROUGE, coverage, novelty, compression ratio, factuality, and human evaluations. |