An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation (D19-1)
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| Challenge: | Existing methods to generate paraphrases are not trivial and often fail in practice. |
| Approach: | They propose to use imitation learning to boost the performance of generating paraphrases by using a pointer-generator model. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the benchmark datasets. |
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Paraphrase Generation with Deep Reinforcement Learning (D18-1)
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| Challenge: | Paraphrase generation is an important but challenging task in natural language processing . traditional symbolic approaches to paraphrase generation include rule-based methods, thesaurus-based approaches and statistical machine translation (SMT) |
| Approach: | They propose a deep reinforcement learning approach to automatic paraphrase generation . they propose supervised learning and reinforcement learning for evaluators . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in paraphrase generation on two datasets. |
Exploring Diverse Expressions for Paraphrase Generation (D19-1)
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| Challenge: | Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. |
| Approach: | They propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. |
| Outcome: | The proposed model gains significant diversity and improves quality over state-of-the-art datasets. |
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards (P19-1)
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| Challenge: | Existing generative models generate too few keyphrases, but they often generate too many . et al. (2017) propose a reinforcement learning approach for keyphrase generation . |
| Approach: | They propose a reinforcement learning approach that encourages a model to generate sufficient keyphrases with an adaptive reward function. |
| Outcome: | The proposed method improves state-of-the-art generative models with conventional and new evaluation methods on real-world datasets. |
Paraphrase Generation: A Survey of the State of the Art (2021.emnlp-main)
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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 . |
| Approach: | They propose to use neural methods to generate fluent, diverse paraphrases from a sentence . they propose to combine large pretrained language models with other mechanisms to generate more advanced paraphrase generation models. |
| Outcome: | This paper examines various approaches to paraphrase generation with a main focus on neural methods. |
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. |
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. |
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. |
| Approach: | They propose a controllable model for semantic and lexical similarities by attaching tags to the head of the input sentence. |
| Outcome: | The proposed model can paraphrase an input sentence according to the tags specified. |
An Empirical Study on Neural Keyphrase Generation (2021.naacl-main)
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| Challenge: | Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. |
| Approach: | They propose to compare the generalizability of KPG models with other models by analyzing the most crucial factors that may affect their generalizarability. |
| Outcome: | The proposed model can be used to predict keyphrases from a set of input sequences, and it can be compared with existing models. |
Neural-Driven Search-Based Paraphrase Generation (2021.eacl-main)
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| Challenge: | Existing non-supervised paraphrase generation models are biased toward specific problems like question answering or image captioning. |
| Approach: | They propose a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance. |
| Outcome: | The proposed algorithms perform well against non-supervised baselines. |
Improving Paraphrase Detection with the Adversarial Paraphrasing Task (2021.acl-long)
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| Challenge: | a new adversarial method of paraphrase identification is being used to identify paraphrases based on word overlap and syntax . authors propose a dataset that generates semantically equivalent but lexically and syntactically disparate paraphrase pairs . |
| Approach: | They propose an adversarial method for paraphrase identification that uses word overlap and syntax to identify paraphrases. |
| Outcome: | The proposed method improves paraphrase detection accuracy and speed of generation of datasets. |