Attribution Analysis of Grammatical Dependencies in LSTMs
Table 2: Top: Number prediction accuracies attained by our model, compared with previously reported results for similar models. Bottom: Pointing game accuracies attained by our model, along with the percentage of examples in which N2 received the highest-magnitude relevance score.
['[BOLD] Prediction Accuracy', '[BOLD] Simple', '[BOLD] IORC (No  [ITALIC] That)', '[BOLD] IORC', '[BOLD] SC', '[BOLD] PP', '[BOLD] SRC', '[BOLD] ORC (No  [ITALIC] That)', '[BOLD] ORC', '[BOLD] SVP', '[BOLD] LVP']
We attribute this to the fact that The appears almost exclusively sentence-initially while the almost never appears sentence-initially; we hypothesize that this difference in distribution provides heuristic information about which nouns are likely to be subjects. While our results are not directly comparable with previous ones, note that our model performs similarly to Kuncoro et al. Shen et al.