| Challenge: | Controlled paraphrase generation produces paraphrases that preserve meaning while allowing precise control over linguistic attributes of output. |
| Approach: | They introduce an encoder-decoder framework that enables fine-grained control over 40 linguistic attributes in English. |
| Outcome: | The proposed framework reduces attribute error by up to 34% over existing models . |
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| Challenge: | LINGCONV is an interactive toolkit for controllable text generation . it allows fine-grained control over 40 specific linguistic attributes spanning lexical, syntactic, and discourse dimensions. |
| Approach: | They propose a toolkit for paraphrase generation that allows finegrained control over 40 specific linguistic attributes. |
| Outcome: | The toolkit is available at https://mohdelgaar-lingconv.hf.space, with a demo video at https:youtu.be/wRBJEJ6EALQ. |
Quality Controlled Paraphrase Generation (2022.acl-long)
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| Challenge: | Recent studies have shown that high quality paraphrases are difficult to generate because of their low flexibility and scalability. |
| Approach: | They propose a quality-guided controlled paraphrase generation model that allows directly controlling the quality dimensions of the generated paraphrase. |
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Controllable Paraphrase Generation for Semantic and Lexical Similarities (2024.lrec-main)
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| Challenge: | Lexically diverse paraphrases are crucial in data augmentation because they enhance the linguistic diversity of the corpus. |
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LingGen: Scalable Multi-Attribute Linguistic Control via Power-Law Masking (2026.eacl-long)
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| Challenge: | Existing methods for controlling coarse attributes are less effective for finer-grained attributes and suffer from inefficiencies when many attributes must be handled jointly. |
| Approach: | They propose a controlled text generation model that allows fine-grained control over a large number of real-valued linguistic attributes. |
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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. |
| Approach: | They propose to add syntactical control to a pretrained language model to generate fluent paraphrases using a retrieval-based selection module. |
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A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation (2020.coling-main)
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| Challenge: | Paraphrase generation is a longstanding problem in natural language processing (NLP) Neural network-based methods have shown great progress on paraphrase generation. |
| Approach: | They propose a framework that integrates variational inference on a target-related latent variable to introduce the diversity. |
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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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Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs (2021.eacl-main)
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| Challenge: | Paraphrase generation requires many annotated paraphrase pairs, which are expensive to obtain. |
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A Plug-and-Play Method for Controlled Text Generation (2021.findings-emnlp)
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| Challenge: | Existing methods for controlling language generation are not able to produce fluent text . current methods require additional models or fine-tuning to ensure specific words are included . |
| Approach: | They propose a plug-and-play decoding method that allows for controlled language generation . they add a shift in the probability distribution over our vocabulary towards semantically similar words . |
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Controllable Paraphrase Generation with a Syntactic Exemplar (P19-1)
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| Challenge: | Prior work on controllable text generation assumes that the generated attribute can take on a finite set of values known a priori. |
| Approach: | They propose a task where the syntax of a generated sentence is controlled rather by a sentential exemplar. |
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