Response Generation by Context-aware Prototype Editing
Table 4: Model ablation tests. Full model denotes Edit-N-Rerank. “-del” means we only consider insertion words. “-ins” means we only consider deletion words. “-both” means we train a standard seq2seq model from prototype response to revised response.
['[EMPTY]', 'Average', 'Extrema', 'Greedy', 'Dis-1', 'Dis-2', 'Originality']
We train variants of our model by removing the insertion word vector, the deletion word vector, and both of them respectively. We can find that embedding based metrics drop dramatically when the editing vector is partially or totally removed, indicating that the edit vector is crucial for response relevance. Diversity and originality do not decrease after the edit vector is removed, implying that the retrieved prototype is the key factor for these two metrics. According to above observations, we conclude that the prototype selector and the context-aware editor play different roles in generating responses.