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Found 4 papers, 1 papers with code
BERTTune: Fine-Tuning Neural Machine Translation with BERTScore
Neural machine translation models are often biased toward the limited translation references seen during training.
RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation
To date, most abstractive summarisation models have relied on variants of the negative log-likelihood (NLL) as their training objective.
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization
Multi-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models.
SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization
In the present day, the predominant approach to this task is to take a performing, pretrained multilingual language model (LM) and fine-tune it for XLS on the language pairs of interest.
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