@inproceedings{nikolaev23:_argadj,
  abstract = {The distinction between arguments and adjuncts is a fundamental
  assumption of several linguistic theories. In this study, we investigate to
  what extent this distinction is picked up by a Transformer-based
  language model. We use BERT as a case study, operationalizing
  arguments and adjuncts as core and non-core FrameNet frame elements,
  respectively, and tying them to activations of
  particular BERT neurons.

  We present evidence, from English and Korean, that BERT learns more
  dedicated representations for arguments than for adjuncts when
  fine-tuned on the FrameNet frame-identification task. We also show that
  this distinction is already present in a weaker form in the vanilla
  pre-trained model.},
  added-at = {2023-04-26T18:18:31.000+0200},
  address = {Nancy, France},
  author = {Nikolaev, Dmitry and Padó, Sebastian},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2a999feae16c6e240857b18253302a552/sp},
  booktitle = {Proceedings of IWCS},
  interhash = {e17b9da7bc7833b7ca6de99aab4a5e32},
  intrahash = {a999feae16c6e240857b18253302a552},
  keywords = {conference myown},
  timestamp = {2024-02-22T12:31:37.000+0100},
  title = {The argument--adjunct distinction in {BERT}: A {FrameNet}-based investigation},
  url = {https://aclanthology.org/2023.iwcs-1.23.pdf},
  year = 2023
}