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Found 4 papers, 2 papers with code
Contextualization and Generalization in Entity and Relation Extraction
In this thesis, we study the behaviour of state-of-the-art models regarding generalization to facts unseen during training in two important Information Extraction tasks: Named Entity Recognition (NER) and Relation Extraction (RE).
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction
State-of-the-art NLP models can adopt shallow heuristics that limit their generalization capability (McCoy et al., 2019).
Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!
Despite efforts to distinguish three different evaluation setups (Bekoulis et al., 2018), numerous end-to-end Relation Extraction (RE) articles present unreliable performance comparison to previous work.
Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
Contextualized embeddings use unsupervised language model pretraining to compute word representations depending on their context.
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