Spying on Your Neighbors: Fine-grained Probing of Contextual Embeddings for Information about Surrounding Words

Josef Klafka, Allyson Ettinger


Abstract
Although models using contextual word embeddings have achieved state-of-the-art results on a host of NLP tasks, little is known about exactly what information these embeddings encode about the context words that they are understood to reflect. To address this question, we introduce a suite of probing tasks that enable fine-grained testing of contextual embeddings for encoding of information about surrounding words. We apply these tasks to examine the popular BERT, ELMo and GPT contextual encoders, and find that each of our tested information types is indeed encoded as contextual information across tokens, often with near-perfect recoverability—but the encoders vary in which features they distribute to which tokens, how nuanced their distributions are, and how robust the encoding of each feature is to distance. We discuss implications of these results for how different types of models break down and prioritize word-level context information when constructing token embeddings.
Anthology ID:
2020.acl-main.434
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4801–4811
Language:
URL:
https://aclanthology.org/2020.acl-main.434
DOI:
10.18653/v1/2020.acl-main.434
Bibkey:
Cite (ACL):
Josef Klafka and Allyson Ettinger. 2020. Spying on Your Neighbors: Fine-grained Probing of Contextual Embeddings for Information about Surrounding Words. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4801–4811, Online. Association for Computational Linguistics.
Cite (Informal):
Spying on Your Neighbors: Fine-grained Probing of Contextual Embeddings for Information about Surrounding Words (Klafka & Ettinger, ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.434.pdf
Video:
 http://slideslive.com/38929325