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Found 7 papers, 1 papers with code
Benchmarking and Improving Detail Image Caption
We also design a more reliable caption evaluation metric called CAPTURE (CAPtion evaluation by exTracting and coUpling coRE information).
Seeing the Image: Prioritizing Visual Correlation by Contrastive Alignment
Despite being simple and effective, this method results in sub-optimal cross-modal alignment by over-emphasizing the text tokens that are less correlated with or even contradictory with the input images.
Extrapolating Multilingual Understanding Models as Multilingual Generators
Considering that encoder-based models have the advantage of efficient generation and self-correction abilities, this paper explores methods to empower multilingual understanding models the generation abilities to get a unified model.
Sentence Representation Learning with Generative Objective rather than Contrastive Objective
Though offering amazing contextualized token-level representations, current pre-trained language models take less attention on accurately acquiring sentence-level representation during their self-supervised pre-training.
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning
If self-supervised learning can be distinguished into two subcategories, generative and contrastive, then most existing studies show that sentence representation learning may more benefit from the contrastive methods but not the generative methods.
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval
In detail, we introduce an in-passage negative sampling strategy to encourage a diverse generation of sentence representations within the same passage.
Graph-free Multi-hop Reading Comprehension: A Select-to-Guide Strategy
Multi-hop reading comprehension (MHRC) requires not only to predict the correct answer span in the given passage, but also to provide a chain of supporting evidences for reasoning interpretability.
