Learning from Explanations with Neural Execution Tree
Table 4: Performance of NLProlog when extracted facts are used as input. Average accuracy over 3 runs is reported. NLProlog empowered by 21 natural language explanations and 5 hand-written rules achieves 1% gain in accuracy.
['[EMPTY]', '|S [ITALIC] a|', '|S [ITALIC] u|', 'Accuracy']
From the result we observe that simply adding the 103 strictly-matched facts is not making notable improvement. However, with the help of NExT, a larger number of structured facts are recognized from support sentences, so that external knowledge from only 21 explanations and 5 rules improve the accuracy by 1 point. This observation validates NExT’s capability in low resource setting and highlight its potential when applied to downstream tasks.