Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach
This addresses the issue of generating more varied paraphrases for NLP applications, but it is incremental as it builds on existing Seq2Seq methods.
The paper tackled the problem of lack of diversity in neural paraphrase generation by proposing BTmPG, a multi-round approach that improved diversity while preserving semantics, as shown by evaluations on two benchmark datasets.
In recent years, neural paraphrase generation based on Seq2Seq has achieved superior performance, however, the generated paraphrase still has the problem of lack of diversity. In this paper, we focus on improving the diversity between the generated paraphrase and the original sentence, i.e., making generated paraphrase different from the original sentence as much as possible. We propose BTmPG (Back-Translation guided multi-round Paraphrase Generation), which leverages multi-round paraphrase generation to improve diversity and employs back-translation to preserve semantic information. We evaluate BTmPG on two benchmark datasets. Both automatic and human evaluation show BTmPG can improve the diversity of paraphrase while preserving the semantics of the original sentence.