Papers with delexicalized version

2 papers
NeuralREG: An end-to-end approach to referring expression generation (P18-1)

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Challenge: Referring Expression Generation models typically rely on features such as salience and grammatical function to make decisions about form and content.
Approach: They propose a new approach that makes decisions about form and content in one go . they use a delexicalized version of the WebNLG corpus to test the approach .
Outcome: The proposed approach significantly improves over two strong baselines.
Towards the Necessity for Debiasing Natural Language Inference Datasets (2020.lrec-1)

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Challenge: Delexicalization of datasets can improve natural language inference performance . a dataset with a delexicalized version of the FEVER dataset is used for natural language learning .
Approach: They propose two techniques for delexicalization that modify annotated datasets to control the importance of lexical entities.
Outcome: The proposed methods maintain performance in-domain and improve performance in some out-of-domain settings.

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