| Challenge: | Using data-to-text generation, text-totext generation and text reduction, we show that conditioning text generation on syntactic constraints permits the generation of syntakically distinct paraphrases for the same input. |
| Approach: | They propose to use four different models for automatic generation of syntactic paraphrases to study the automatic generation process. |
| Outcome: | The proposed models can generate syntactic paraphrases for the same input and exploit different types of input to increase the number of distinct paraphrased for a given input. |
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| Challenge: | Using neural models, paraphrase generation research has shifted to neural methods . a recent study focused on paraphrases, which are used in language understanding tasks . |
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Paraphrase Types for Generation and Detection (2023.emnlp-main)
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| Challenge: | Current approaches to paraphrase generation and detection ignore the intricate linguistic properties of language. |
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Paraphrase Generation and Evaluation on Colloquial-Style Sentences (2020.lrec-1)
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Controllable Paraphrase Generation for Semantic and Lexical Similarities (2024.lrec-main)
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Semi-Supervised Learning for Neural Keyphrase Generation (D18-1)
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Syntax-Guided Controlled Generation of Paraphrases (2020.tacl-1)
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| Challenge: | Recent work has explored the incorporation of complex syntactic-guidance as constraints in the task of controlled text generation. |
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AESOP: Paraphrase Generation with Adaptive Syntactic Control (2021.emnlp-main)
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| Challenge: | Existing models for paraphrase generation use fixed syntactic structures for all input sentences. |
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| Challenge: | Paraphrase generation is a long-standing task in natural language processing (NLP). |
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Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)
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| Challenge: | In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria. |
| Approach: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks that takes a piece of text as input and generates a revision that is improved according to some specific criteria. |
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Negative Lexically Constrained Decoding for Paraphrase Generation (P19-1)
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| Challenge: | Paraphrase generation is a monolingual machine translation problem. |
| Approach: | They propose a neural model that first identifies words in the source sentence that should be paraphrased and then decodes them by negative lexical constraints. |
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