Learning from Explanations with Neural Execution Tree
Table 12: BERT experiments on Restaurant dataset using 45 and 75 explanations
['[EMPTY]', '45', '75']
Our framework is model-agnostic as it can be integrated with any downstream classifier. Results show that our model still outperforms baseline methods when BERT is incorporated. We observe the performance of NExT is approaching the upper bound 85% (by feeding all data to BERT), with only 75 explanations, which again demonstrates the annotation efficiency of NExT.