Analyzing Individual Neurons in Pre-trained Language Models

Nadir Durrani, Hassan Sajjad, Fahim Dalvi, Yonatan Belinkov


Abstract
While a lot of analysis has been carried to demonstrate linguistic knowledge captured by the representations learned within deep NLP models, very little attention has been paid towards individual neurons.We carry outa neuron-level analysis using core linguistic tasks of predicting morphology, syntax and semantics, on pre-trained language models, with questions like: i) do individual neurons in pre-trained models capture linguistic information? ii) which parts of the network learn more about certain linguistic phenomena? iii) how distributed or focused is the information? and iv) how do various architectures differ in learning these properties? We found small subsets of neurons to predict linguistic tasks, with lower level tasks (such as morphology) localized in fewer neurons, compared to higher level task of predicting syntax. Our study also reveals interesting cross architectural comparisons. For example, we found neurons in XLNet to be more localized and disjoint when predicting properties compared to BERT and others, where they are more distributed and coupled.
Anthology ID:
2020.emnlp-main.395
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4865–4880
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.395
DOI:
10.18653/v1/2020.emnlp-main.395
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.395.pdf