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Found 3 papers, 1 papers with code
Does it Really Generalize Well on Unseen Data? Systematic Evaluation of Relational Triple Extraction Methods
To keep a knowledge graph up-to-date, an extractor needs not only the ability to recall the triples it encountered during training, but also the ability to extract the new triples from the context that it has never seen before.
Stop just recalling memorized relations: Extracting Unseen Relational Triples from the context
In this paper, we show that although existing extraction models are able to memorize and recall already seen triples, they cannot generalize effectively for unseen triples.
Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks
Among many approaches, the simplest and most intuitive way is zero imputation, which treats the value of a missing entry simply as zero.
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