Language Technologies Institute Seminar

  • Remote Access Enabled - Zoom
  • Virtual Presentation
  • ELENA VOITA
  • Ph.D. Student
  • Insitute for Language, Cognition and Computation, School of Informatics
  • University of Edinburgh
Seminars

Information-Theoretic Probing with Minimum Description Length

How can you know whether a model has learned to encode a linguistic property? The most popular approach to measure how well pretrained representations encode a linguistic property is to use the accuracy of a probing classifier (probe). However, such probes often fail to adequately reflect differences in representations, and they can show different results depending on probe hyperparameters. As an alternative to standard probing, we propose information-theoretic probing which measures minimum description length (MDL) of labels given representations. In addition to probe quality, the description length evaluates “the amount of effort” needed to achieve this quality. We show that (i) MDL can be easily evaluated on top of standard probe-training pipelines, and (ii) compared to standard probes, the results of MDL probing are more informative, stable, and sensible.

Elena (Lena) Voita, is a Ph.D. student at the University of Edinburgh and University of Amsterdam, where she is very happy to be supervised by Ivan Titov and Rico Sennrich and iscurrently a Facebook PhD Fellow.   Her research focuses on document-level neural machine translation, as well as on understanding what and how neural models learn.  Previously, she was a research scientist at Yandex Research and worked closely with the Yandex Translate team, where she continues to teach NLP at the Yandex School of Data Analysis.

Faculty Host: Graham Neubig

Zoom Participation. See announcement. 

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