9 citations · 12 across the 4 of their papers we have counts for
9 papers
Conditional probing: measuring usable information beyond a baseline
John Hewitt, Kawin Ethayarajh, Percy Liang +1
Probing experiments investigate the extent to which neural representations make properties -- like part-of-speech -- predictable. One suggests that a representation encodes a prope…
Refining Targeted Syntactic Evaluation of Language Models
Benjamin Newman, Kai-Siang Ang, Julia Gong +1
Targeted syntactic evaluation of subject-verb number agreement in English (TSE) evaluates language models' syntactic knowledge using hand-crafted minimal pairs of sentences that di…
Probing artificial neural networks: insights from neuroscience
Anna A. Ivanova, John Hewitt, Noga Zaslavsky
A major challenge in both neuroscience and machine learning is the development of useful tools for understanding complex information processing systems. One such tool is probes, i.…
RNNs can generate bounded hierarchical languages with optimal memory
John Hewitt, Michael Hahn, Surya Ganguli +2
Recurrent neural networks empirically generate natural language with high syntactic fidelity. However, their success is not well-understood theoretically. We provide theoretical in…
The EOS Decision and Length Extrapolation
Benjamin Newman, John Hewitt, Percy Liang +1
Extrapolation to unseen sequence lengths is a challenge for neural generative models of language. In this work, we characterize the effect on length extrapolation of a modeling dec…
Finding Universal Grammatical Relations in Multilingual BERT
Ethan A. Chi, John Hewitt, Christopher D. Manning
Recent work has found evidence that Multilingual BERT (mBERT), a transformer-based multilingual masked language model, is capable of zero-shot cross-lingual transfer, suggesting th…