Papers with Paraphrase generation
Transformer and seq2seq model for Paraphrase Generation (D19-56)
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| Challenge: | Existing methods for generating paraphrases fall into one of these broad categories -rule-based, seq2seq, deep generative models and a varied combination. |
| Approach: | They propose a framework that combines transformer and sequence-to-sequence models for better quality of generated paraphrases. |
| Outcome: | The proposed framework improves on two datasets-QUORA and MSCOCO using transformer and sequence-to-sequence models. |
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. |
| Outcome: | The proposed method generates paraphrases which maintain original meaning while achieving higher diversity than the uncontrolled baseline. |
Keep the Primary, Rewrite the Secondary: A Two-Stage Approach for Paraphrase Generation (2021.findings-acl)
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| Challenge: | Existing approaches to generate paraphrases are decomposable, but some use a sequence-to-sequence model to generate each word in a uniform way. |
| Approach: | They propose a framework for identification then aggregation of input tokens and a custom decoder to generate paraphrases. |
| Outcome: | The proposed framework outperforms previous studies on two benchmark datasets and generates paraphrases in interpretable and controllable way. |
Learning to Adapt to Low-Resource Paraphrase Generation (2022.emnlp-main)
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| Challenge: | Conventional approaches to paraphrase generation often rely on a large number of parallel paraphrases, which require a lot of domain knowledge. |
| Approach: | They propose an adapter for paraphrase generation models optimized by meta-learning to overcome domain shifting problem when training on scarce labeled data. |
| Outcome: | The proposed model achieves state-of-the-art on three benchmark datasets. |
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. |
| Approach: | They propose a model that learns to disentangle the semantics and syntax of a sentence from unannotated texts. |
| Outcome: | The proposed model learns to disentangle the semantics and syntax of a sentence from a collection of unannotated texts. |
Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering (2021.findings-emnlp)
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| Challenge: | Document-level paraphrase generation is an important task in natural language processing. |
| Approach: | They propose a coherence relationship-guided paraphrase generation model that leverages graph GRU to encode the coherency relationship graph and get the cohesion-aware representation for each sentence. |
| Outcome: | The proposed model outperforms baseline models on BERTScore and diversity scores. |
Multi-task Learning for Paraphrase Generation With Keyword and Part-of-Speech Reconstruction (2022.findings-acl)
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| Challenge: | PGKPR is a deep learning approach to generate paraphrases using key semantics of the source sentence. |
| Approach: | They propose a model with keyword and part-of-speech reconstruction for paraphrase generation using deep learning. |
| Outcome: | The proposed model outperforms comparative models on two commonly-used datasets. |
Learning to Selectively Learn for Weakly Supervised Paraphrase Generation with Model-based Reinforcement Learning (2022.naacl-main)
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| Challenge: | Paraphrase generation is an important natural language generation task . however, the effectiveness of paraphrase generation can be limited due to the limited data available. |
| Approach: | They propose a weakly supervised approach to paraphrase generation that leverages reinforcement learning for effective model training with data selection. |
| Outcome: | The proposed model improves the state-of-the-art performance on four weakly supervised paraphrase generation tasks. |
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. |
| Outcome: | The proposed framework outperforms baseline models on the metrics based on n-gram matching and semantic similarity, and it can generate multiple different paraphrases by assembling different syntactic variables. |
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning (2020.coling-main)
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| Challenge: | Paraphrase generation is of great importance for many downstream tasks in natural language processing. |
| Approach: | They propose a method to generate sentences as learning objectives from the learned data distribution and employ reinforcement learning to combine these new learning objectives for model training. |
| Outcome: | The proposed method gains significant diversity and improves generation quality over state-of-the-art datasets. |
Unsupervised Paraphrasing with Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Paraphrase generation has benefited from recent advances in the design of training objectives and model architectures, but previous studies focused on supervised methods that require a large amount of labeled data that is costly to collect. |
| Approach: | They propose a transfer learning approach that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. |
| Outcome: | The proposed model performs state-of-the-art on the Quora Question Pair and ParaNMT datasets and is robust to domain shift between the two datasets. |
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation (2023.acl-long)
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| Challenge: | Paraphrase generation is a long-standing task in natural language processing (NLP). |
| Approach: | They propose to generate large-scale syntactically diverse paraphrase datasets by abstract meaning representation back-translation. |
| Outcome: | The proposed dataset is syntactically more diverse than existing datasets while maintaining good semantic similarity. |
ParaMac: A General Unsupervised Paraphrase Generation Framework Leveraging Semantic Constraints and Diversifying Mechanisms (2022.findings-emnlp)
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| Challenge: | Existing unsupervised methods for paraphrase generation are weak in semantic equivalence or expression diversity. |
| Approach: | They propose a framework for unsupervised paraphrase generation that employs multi-aspect equivalence constraints and multi-granularity diversifying mechanisms to achieve good semantic equvalence and expressive diversity. |
| Outcome: | The proposed framework achieves 9.1% and 3.3% absolute gains over previous SOTA on Quora and MSCOCO and can improve to 18.0% and 4.6% on GLUE. |
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation (2021.emnlp-main)
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| Challenge: | Existing approaches to generate paraphrases with weak supervision are limited in real-world scenarios due to the lack of coherent and controllable generated paraphrase. |
| Approach: | They propose a method to generate high-quality paraphrases with weak supervision . they obtain abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion . |
| Outcome: | The proposed approach achieves significant improvements over existing methods and is even comparable in performance with supervised state-of-the-arts. |
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. |
| Outcome: | The proposed model improves paraphrase generation by making necessary rewrites to an input sentence. |
Polly Want a Cracker: Analyzing Performance of Parroting on Paraphrase Generation Datasets (D19-1)
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| Challenge: | Paraphrase generation is an interesting and challenging task which has numerous practical applications. |
| Approach: | They analyze datasets commonly used for paraphrase generation research and show that simply parroting input sentences surpasses state-of-the-art models when evaluated on standard metrics. |
| Outcome: | The proposed model can generate paraphrases even without making any changes to the input sentence or even none at all, compared with other models. |