Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Table 4: ROUGE F1 score on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based.
['[BOLD] LCSTS', '[BOLD] R-1', '[BOLD] R-2', '[BOLD] R-L']
We report the ROUGE F1 score of our model and the baseline models on the test sets. Our model achieves the score of 37.8 ROUGE-1, 25.6 ROUGE-2, and 35.2 ROUGE-L, outperforming all of the previous models. First, we compare our model with the sequence-to-sequence model. It shows that our model significant outperforms the sequence-to-sequence baseline with a large margin of 5.7 ROUGE-1, 5.7 ROUGE-2, and 6.0 ROUGE-L. Then, we compare our model with other related models. The state-of-the-art model is DRGD Li et al. Our model has a relative gain of 0.8 ROUGE-1, 1.4 ROUGE-2 and 1.0 ROUGE-L over the state-of-the-art models.