Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Table 3: Human evaluation of our model and other related systems on PWKP and EW-SEW datasets. The results are reported on the test sets.
['PWKP', 'Fluency', 'Adequacy', 'Simplicity', 'All']
DRESS-LS is based on the reinforcement learning, and it encourages the fluency, simplicity and relevance of the outputs. Therefore, it achieves a high score in our human evaluation. WEAN gains a even better score than DRESS-LS. Besides, WEAN generates more adequate and simpler outputs than the reference on PWKP. The predictions of SBMT-SARI are the most adequate among the compared systems on EW-SEW. In general, WEAN outperforms all of the other systems, considering the balance of fluency, adequate and simplicity. We conduct significance tests based on t-test. The significance tests suggest that WEAN has a very significant improvement over baseline, with p≤0.001 over DRESS-LS in all of the dimension on PWKP, p≤0.05 over DRESS-LS in the dimension of fluency , p≤0.005 over NTS-w2v in the dimension of simplicity and p≤0.005 over DRESS-LS in the dimension of all.