Paraphrase Generation with Latent Bag of Words

• URL: http://arxiv.org/abs/2001.01941v1
• Date: Tue, 7 Jan 2020 09:22:58 GMT
• Title: Paraphrase Generation with Latent Bag of Words
• Abstract summary: We propose a latent bag of words (BOW) model for paraphrase generation. We use source words to predict their neighbors and model the target BOW with a mixture of softmax. Our latent BOW model not only enhances the decoder, but also exhibits clear interpretability.
• Score: 46.34571236204907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Paraphrase generation is a longstanding important problem in natural language processing. In addition, recent progress in deep generative models has shown promising results on discrete latent variables for text generation. Inspired by variational autoencoders with discrete latent structures, in this work, we propose a latent bag of words (BOW) model for paraphrase generation. We ground the semantics of a discrete latent variable by the BOW from the target sentences. We use this latent variable to build a fully differentiable content planning and surface realization model. Specifically, we use source words to predict their neighbors and model the target BOW with a mixture of softmax. We use Gumbel top-k reparameterization to perform differentiable subset sampling from the predicted BOW distribution. We retrieve the sampled word embeddings and use them to augment the decoder and guide its generation search space. Our latent BOW model not only enhances the decoder, but also exhibits clear interpretability. We show the model interpretability with regard to \emph{(i)} unsupervised learning of word neighbors \emph{(ii)} the step-by-step generation procedure. Extensive experiments demonstrate the transparent and effective generation process of this model.\footnote{Our code can be found at \url{https://github.com/FranxYao/dgm_latent_bow}}

Related papers

• AURORA-LM: Autoencoding Unified Representation for Continuous-Latent Diffusion Language Modeling [60.04959047453115]
  AURORA-LM is a continuous-latent language model that separates the construction of a decodable text representation from the modeling of its distribution.<n>AURORA-LM achieves the strongest performance among evaluated continuous and diffusion-based language models on OpenWebText free generation and XSum summarization.
  arXiv  Detail & Related papers  (2026-08-03T17:59:50Z)
• Large Language Models Explore by Latent Distilling [17.44180253276711]
  ESamp is a decoding approach that explicitly encourages semantic diversity during generation.<n> ESamp achieves robust generalization across mathematics, science, and code generation benchmarks.
  arXiv  Detail & Related papers  (2026-04-27T19:08:13Z)
• Self-Corrected Image Generation with Explainable Latent Rewards [55.29175717238288]
  We propose xLARD, a self-correcting framework that guides generation through Explainable LAtent RewarDs.<n>xLARD introduces a lightweight corrector that refines latent representations based on structured feedback from model-generated references.<n> Experiments show that xLARD improves semantic alignment and visual fidelity while maintaining generative priors.
  arXiv  Detail & Related papers  (2026-03-26T02:59:35Z)
• Vector-Quantized Prompt Learning for Paraphrase Generation [18.40940464497253]
  This paper proposes to generate diverse and high-quality paraphrases by exploiting the pre-trained models with instance-dependent prompts. Extensive experiments demonstrate that the proposed method achieves new state-of-art results on three benchmark datasets.
  arXiv  Detail & Related papers  (2023-11-25T07:13:06Z)
• RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling [79.56442336234221]
  We introduce RegaVAE, a retrieval-augmented language model built upon the variational auto-encoder (VAE) It encodes the text corpus into a latent space, capturing current and future information from both source and target text. Experimental results on various datasets demonstrate significant improvements in text generation quality and hallucination removal.
  arXiv  Detail & Related papers  (2023-10-16T16:42:01Z)
• Learning Multiscale Transformer Models for Sequence Generation [33.73729074207944]
  We build a multiscale Transformer model by establishing relationships among scales based on word-boundary information and phrase-level prior knowledge. Notably, it yielded consistent performance gains over the strong baseline on several test sets without sacrificing the efficiency.
  arXiv  Detail & Related papers  (2022-06-19T07:28:54Z)
• A Sparsity-promoting Dictionary Model for Variational Autoencoders [16.61511959679188]
  Structuring the latent space in deep generative models is important to yield more expressive models and interpretable representations. We propose a simple yet effective methodology to structure the latent space via a sparsity-promoting dictionary model.
  arXiv  Detail & Related papers  (2022-03-29T17:13:11Z)
• Generative Text Modeling through Short Run Inference [47.73892773331617]
  The present work proposes a short run dynamics for inference. It is variation from the prior distribution of the latent variable and then runs a small number of Langevin dynamics steps guided by its posterior distribution. We show that the models trained with short run dynamics more accurately model the data, compared to strong language model and VAE baselines, and exhibit no sign of posterior collapse.
  arXiv  Detail & Related papers  (2021-05-27T09:14:35Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• Preventing Posterior Collapse with Levenshtein Variational Autoencoder [61.30283661804425]
  We propose to replace the evidence lower bound (ELBO) with a new objective which is simple to optimize and prevents posterior collapse. We show that Levenstein VAE produces more informative latent representations than alternative approaches to preventing posterior collapse.
  arXiv  Detail & Related papers  (2020-04-30T13:27:26Z)
• Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck [52.08901549360262]
  Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. VAEs tend to ignore latent variables with a strong auto-regressive decoder. We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
  arXiv  Detail & Related papers  (2020-04-22T14:41:37Z)
• On the Discrepancy between Density Estimation and Sequence Generation [92.70116082182076]
  log-likelihood is highly correlated with BLEU when we consider models within the same family. We observe no correlation between rankings of models across different families.
  arXiv  Detail & Related papers  (2020-02-17T20:13:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.