In consistent with previous study (Gu et al., 2019), we compute the similarity between the bag-of-words (BOW) embeddings representations of generated results and reference.
we calculate three metrics:1) Greedy (BOW-Greedy), i.e., greedily matching words in two utterances based on the cosine similarities, and the total scores is then averaged across all words (Rus and Lintean, 2012); 2) Average (BOW-Average), cosine similarity between the averaged word embeddings in the two utterances (Mitchell and Lapata, 2008); 3) Extrema (BOW-Extrema),  cosine similarity between the largest extreme values among the word embeddings in the two utterances (Forgues et al., 2014).