Revisiting Round-Trip Translation for Quality Estimation
Table 8: AUC scores of precision-recall curves of Bert-based metrics on PAWSWiKi and PAWSQQP testing set.
['[BOLD] Metrics', 'PAWSWiKi', 'PAWSQQP']
The results from all the previous sections consistently show the outstanding performance of RTT-SBert and RTT-BERTScore. We see this in a view of paraphrase detection ability of SBert and BERTScore. To confirm our assumption, we compare the area-under-curve (AUC) scores of precision-recall curves of the four metrics used to measure input and RTT on PAWS dataset. The higher the score is, the better the metric at paraphrase detection. Note that SBert indicates the cosine similarity of the embedding vectors of two sentence pairs extracted from the model.