| Challenge: | Existing models of paraphrase generation are based on a syntactic sketch, but prior work has included inductive bias. |
| Approach: | They propose a method for learning decompositions of dense encodings as a sequence of discrete latent variables that make iterative refinements of increasing granularity. |
| Outcome: | The proposed model improves on human paraphrase generation by predicting syntactic sketches at test time. |
Similar Papers
Hierarchical Quantized Representations for Script Generation (D18-1)
Copied to clipboard
| Challenge: | Scripts define knowledge about how everyday scenarios are expected to unfold . language models tend towards local coherency, which is a major issue . |
| Approach: | They propose an autoencoder model with a latent space defined by a hierarchy of categorical variables . they use a vector quantization based approach which allows continuous embeddings to be associated with each latent variable value . |
| Outcome: | The proposed model outperforms a language modeling-based method on several tasks and lowers perplexity scores. |
An End-to-End Generative Architecture for Paraphrase Generation (D19-1)
Copied to clipboard
| Challenge: | Existing methods for generating paraphrases with linguistic knowledge are often domain specific and hard to scale, or yield inferior results. |
| Approach: | They propose an end-to-end conditional generative architecture for generating paraphrases via adversarial training which does not depend on extra linguistic information. |
| Outcome: | The proposed method outperforms existing models on automatic metrics and human evaluations on four public datasets. |
Vector-Quantized Prompt Learning for Paraphrase Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for paraphrase generation are difficult to understand and generate. |
| Approach: | They propose to generate diverse paraphrases by using instance-dependent prompts to control the generation of pre-trained models. |
| Outcome: | The proposed method achieves state-of-the-art on three benchmark datasets, including Quora, Wikianswers, and MSCOCO. |
Syntax-Infused Variational Autoencoder for Text Generation (P19-1)
Copied to clipboard
| Challenge: | Experimental results demonstrate the generative superiority of SIVAE on both reconstruction and targeted syntactic evaluations. |
| Approach: | They propose a syntax-infused variational autoencoder that integrates sentences with their syntactic trees to improve the grammar of generated sentences. |
| Outcome: | The proposed model improves the grammar of generated sentences by integrating sentences with syntactic trees. |
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation (2020.coling-main)
Copied to clipboard
| 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. |
Controllable Paraphrase Generation with a Syntactic Exemplar (P19-1)
Copied to clipboard
| 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. |
| Outcome: | The proposed model achieves improvements over baselines and learns to capture desirable characteristics. |
Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on syntactically controlled paraphrase generation rely on large-scale parallel data. |
| Approach: | They propose a syntactically-informed unsupervised paraphrasing model based on conditional variational auto-encoder which can generate texts in a specified syntastic structure. |
| Outcome: | The proposed model can generate diverse paraphrases with specified syntactic structure using non-parallel data. |
Generating Syntactic Paraphrases (D18-1)
Copied to clipboard
| 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. |
AESOP: Paraphrase Generation with Adaptive Syntactic Control (2021.emnlp-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed model achieves state-of-the-art on semantic preservation and syntactic conformation on two benchmark datasets with ground-truth syntaktic control from human-annotated exemplars. |
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. |
| Approach: | They propose a Variational Hierarchical Dialog Autoencoder for modeling the complete aspects of goal-oriented dialogs using inter-connected latent variables and learns to generate coherent dialogs from the latent spaces. |
| Outcome: | The proposed model outperforms previous strong baselines on dialog response generation and user simulation tasks. |