Picking BERT’s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis
By Michael A. Lepori, R. Thomas McCoy. 2020
27 citations recorded by the source corpus.
As the name implies, contextualized representations of language are typically motivated by their ability to encode context. Which aspects of context are captured by such representations? We introduce an approach to address this question using Representational Similarity Analysis (RSA). As case studies, we investigate the degree to which a verb embedding encodes the verb’s subject, a pronoun embedding encodes the pronoun’s antecedent, and a full-sentence representation encodes the sentence’s head word (as determined by a dependency parse). In all cases, we show that BERT’s contextualized embeddings reflect the linguistic dependency being studied, and that BERT encodes these dependencies to a greater degree than it encodes less linguistically-salient controls. These results demonstrate the ability of our approach to adjudicate between hypotheses about which aspects of context are encoded in representations of language.
Subfields: Artificial Intelligence, Cultural Studies
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yonatan Belinkov
- James R. Glass
- Nadir Durrani
- Hassan Sajjad
- Fahim Dalvi
- Percy Liang
- John Hewitt
- Ellie Pavlick
In Cultural Studies
Citations in the corpus
- Probing Classifiers: Promises, Shortcomings, and Alternatives (2021, background): “…Mur, and Bandettini 2008) to measure similarity between word or sentence representations and syntactic properties, both local properties like determining a verb’s subject (Lepori and McCoy 2020) and more structured properties like inferring the full syntactic tree (Chrupała and Alishahi 2019).”