Papers with neural paraphrasing model
Neural Syntactic Preordering for Controlled Paraphrase Generation (2020.acl-main)
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| Challenge: | Existing approaches to paraphrasing natural language sentences are limited by the complexity of the task. |
| Approach: | They propose a framework for paraphrasing natural language sentences that uses syntactic transformations to softly "reorder" the source sentence and their proposed system is evaluated automatically and by humans . |
| Outcome: | The proposed model retains the quality of the baseline approaches while giving a substantial increase in the diversity of the generated paraphrases. |
Controllable Text Simplification with Explicit Paraphrasing (2021.naacl-main)
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| Challenge: | Existing text simplification systems rely on deletion and do not paraphrase well. |
| Approach: | They propose a hybrid approach that leverages linguistically-motivated rules for splitting and deletion and couples them with a neural paraphrasing model to produce varied rewriting styles. |
| Outcome: | The proposed model improves paraphrasing capability and paraphrases more often than existing models. |
Improving the Robustness of Question Answering Systems to Question Paraphrasing (P19-1)
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| Challenge: | Despite advancement of question answering systems, generalizability of QA models is a topic of concern. |
| Approach: | They propose to use a neural paraphrasing model to generate multiple paraphrased questions for a given source question and a set of paraphrase suggestions to re-train the models. |
| Outcome: | The proposed approach requires no human intervention to re-train the models for improved robustness to question paraphrasing. |