46 research outputs found
Higher-order Comparisons of Sentence Encoder Representations
Representational Similarity Analysis (RSA) is a technique developed by
neuroscientists for comparing activity patterns of different measurement
modalities (e.g., fMRI, electrophysiology, behavior). As a framework, RSA has
several advantages over existing approaches to interpretation of language
encoders based on probing or diagnostic classification: namely, it does not
require large training samples, is not prone to overfitting, and it enables a
more transparent comparison between the representational geometries of
different models and modalities. We demonstrate the utility of RSA by
establishing a previously unknown correspondence between widely-employed
pretrained language encoders and human processing difficulty via eye-tracking
data, showcasing its potential in the interpretability toolbox for neural
modelsComment: EMNLP 201
How Do BERT embeddings organize linguistic knowledge?
Several studies investigated the linguistic information implicitly encoded in Neural Language Models. Most of these works focused on quantifying the amount and type of information available within their internal representations and across their layers. In line with this scenario, we proposed a different study, based on Lasso regression, aimed at understanding how the information encoded by BERT sentence-level representations is arranged within its hidden units. Using a suite of several probing tasks, we showed the existence of a relationship between the implicit knowledge learned by the model and the number of individual units involved in the encodings of this competence. Moreover, we found that it is possible to identify groups of hidden units more relevant for specific linguistic properties. © 2021 Association for Computational Linguistics
Measuring Memorization Effect in Word-Level Neural Networks Probing
Multiple studies have probed representations emerging in neural networks
trained for end-to-end NLP tasks and examined what word-level linguistic
information may be encoded in the representations. In classical probing, a
classifier is trained on the representations to extract the target linguistic
information. However, there is a threat of the classifier simply memorizing the
linguistic labels for individual words, instead of extracting the linguistic
abstractions from the representations, thus reporting false positive results.
While considerable efforts have been made to minimize the memorization problem,
the task of actually measuring the amount of memorization happening in the
classifier has been understudied so far. In our work, we propose a simple
general method for measuring the memorization effect, based on a symmetric
selection of comparable sets of test words seen versus unseen in training. Our
method can be used to explicitly quantify the amount of memorization happening
in a probing setup, so that an adequate setup can be chosen and the results of
the probing can be interpreted with a reliability estimate. We exemplify this
by showcasing our method on a case study of probing for part of speech in a
trained neural machine translation encoder.Comment: Accepted to TSD 2020. Will be published in Springer LNC