Deep contextualized word representations
Peters et al., 2018, Deep contextualized word representations (Proceedings of the 2018 Conference of the North American Chapter of ACL: Human Language Technologies (NAACL-HLT 2018))
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5.1 Alternate layer weighting schemes
- Does combining all biLM layers beat using only the top layer?Yes. On SQuAD dev, the top layer alone added 3.9 F1 over the baseline; averaging all layers added 0.3 more, and learning individual layer weights (small regularization \(\lambda = 0.001\)) another 0.2. SNLI and SRL showed the same trend; CoVe benefited similarly but less.
- Peters et al., 2018, Deep contextualized word representations (Proceedings of the 2018 Conference of the North American Chapter of ACL: Human Language Technologies (NAACL-HLT 2018)), §5.1, p. 2232
Source
- Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018), pp. 2227–2237. License: CC BY 4.0. Flashcards adapted from this work. doi:10.18653/v1/N18-1202 https://aclanthology.org/N18-1202/