mlsanityData to 7 Oct 2026

Extrapolation in NLP

arXiv 1805.06648 ↗First posted 17 May 2018cs.CLcited by 2

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Abstract (arXiv)

We argue that extrapolation to examples outside the training space will often be easier for models that capture global structures, rather than just maximise their local fit to the training data. We show that this is true for two popular models: the Decomposable Attention Model and word2vec.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2018.

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Citing papers per year: 2021: 1; 2022: 0; 2023: 1.

2 papers.

  1. 2023Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encodingcited by 32
  2. 2021Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modellingcited by 0