@InProceedings{singh-declercq-lefever:2022:SIGUL,
  author    = {Singh, Pranaydeep  and  De Clercq, Orphee  and  Lefever, Els},
  title     = {Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages},
  booktitle      = {Proceedings of the the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages},
  month          = {June},
  year           = {2022},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {176--184},
  abstract  = {This paper reports on experiments for cross-lingual transfer using the anchor-based approach of Schuster et al.~(2019) for English and a low-resourced language, namely Hindi. For the sake of comparison, we also evaluate the approach on three very different higher-resourced languages, viz.~Dutch, Russian and Chinese. Initially designed for ELMo embeddings, we analyze the approach for the more recent BERT family of transformers for a variety of tasks, both mono and cross-lingual. The results largely prove that like most other cross-lingual transfer approaches, the static anchor approach is underwhelming for the low-resource language, while performing adequately for the higher resourced ones. We attempt to provide insights into both the quality of the anchors, and the performance for low-shot cross-lingual transfer to better understand this performance gap. We make the extracted anchors and the modified train and test sets available for future research at https://github.com/pranaydeeps/Vyaapak},
  url       = {https://aclanthology.org/2022.sigul-1.23}
}