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• W3034531294 abstract "Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization. Past approaches struggle to cover this space of paraphrase possibilities in an interpretable manner. Our work, inspired by pre-ordering literature in machine translation, uses syntactic transformations to softly “reorder” the source sentence and guide our neural paraphrasing model. First, given an input sentence, we derive a set of feasible syntactic rearrangements using an encoder-decoder model. This model operates over a partially lexical, partially syntactic view of the sentence and can reorder big chunks. Next, we use each proposed rearrangement to produce a sequence of position embeddings, which encourages our final encoder-decoder paraphrase model to attend to the source words in a particular order. Our evaluation, both automatic and human, shows that the proposed system retains the quality of the baseline approaches while giving a substantial increase in the diversity of the generated paraphrases." @default.
• W3034531294 created "2020-06-19" @default.
• W3034531294 creator A5015133105 @default.
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• W3034531294 date "2020-01-01" @default.
• W3034531294 modified "2023-10-16" @default.
• W3034531294 title "Neural Syntactic Preordering for Controlled Paraphrase Generation" @default.
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• W3034531294 doi "https://doi.org/10.18653/v1/2020.acl-main.22" @default.
• W3034531294 hasPublicationYear "2020" @default.
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