Neural Machine Translation For Paraphrase Generation
Table 1: Paraphrases evaluation on Stubhub skill. Baseline has the results for ASK models trained on the sampled grammar only. Other models are augmenting the samples with paraphrases. Test dataset (annotated live data) consists of 635 utterances. Input/output size is the number of original and generated utterances.
['[BOLD] Model', '[BOLD] Bigram coverage', '[BOLD] Input/output size', '[BOLD] Relative error change over baseline  [BOLD] ICER', '[BOLD] Relative error change over baseline  [BOLD] SER', '[BOLD] Relative error change over baseline  [BOLD] SEMER']
All tested skills have four or more times improvement on the bigram coverage score, compared to the corresponding skills baselines. Data augmentation has better effect on the IC than SER. Data augmentation was done with the fixed encoder model trained on out-of-domain corpus.