6 citations · 10 across the 8 of their papers we have counts for
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cs.LG2020
Evaluating Attribution Methods using White-Box LSTMs
Yiding Hao
Interpretability methods for neural networks are difficult to evaluate because we do not understand the black-box models typically used to test them. This paper proposes a framewor…
cs.CL2020
Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling
Yiding Hao, Simon Mendelsohn, Rachel Sterneck +2
By positing a relationship between naturalistic reading times and information-theoretic surprisal, surprisal theory (Hale, 2001; Levy, 2008) provides a natural interface between la…
cs.CL2020★ 3 cited
Attribution Analysis of Grammatical Dependencies in LSTMs
Yiding Hao
LSTM language models have been shown to capture syntax-sensitive grammatical dependencies such as subject-verb agreement with a high degree of accuracy (Linzen et al., 2016, inter…