565 citations · 1.6k across the 82 of their papers we have counts for
9 papers · 1 filter
Learning from others' mistakes: Avoiding dataset biases without modeling them
Victor Sanh, Thomas Wolf, Yonatan Belinkov +1
State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying…
Analyzing Individual Neurons in Pre-trained Language Models
Nadir Durrani, Hassan Sajjad, Fahim Dalvi +1
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 tow…
Similarity Analysis of Self-Supervised Speech Representations
Yu-An Chung, Yonatan Belinkov, James Glass
Self-supervised speech representation learning has recently been a prosperous research topic. Many algorithms have been proposed for learning useful representations from large-scal…
Probing Neural Dialog Models for Conversational Understanding
Abdelrhman Saleh, Tovly Deutsch, Stephen Casper +2
The predominant approach to open-domain dialog generation relies on end-to-end training of neural models on chat datasets. However, this approach provides little insight as to what…
The Sensitivity of Language Models and Humans to Winograd Schema Perturbations
Mostafa Abdou, Vinit Ravishankar, Maria Barrett +3
Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sens…
Similarity Analysis of Contextual Word Representation Models
John M. Wu, Yonatan Belinkov, Hassan Sajjad +3
This paper investigates contextual word representation models from the lens of similarity analysis. Given a collection of trained models, we measure the similarity of their interna…