Keep the Primary, Rewrite the Secondary: A Two-Stage Approach for Paraphrase Generation
Yixuan Su, David Vandyke, Simon Baker, Yan Wang, Nigel Collier · 2021
Paraphrase generation is an important and challenging NLG problem.In this work, we propose a new Identification-then-Aggregation (IA) framework to tackle this task.In the identification step, the input tokens are sorted into two groups by a novel Primary/Secondary Identification (PSI) algorithm.In the aggregation step, these groups are separately encoded, before being aggregated by a custom designed decoder, which autoregressively generates the paraphrased sentence.In extensive experiments on two benchmark datasets, we demonstrate that our model outperforms previous studies by a notable margin.We also show that the proposed approach can generate paraphrases in an interpretable and controllable way.