SparseGAN: Sparse Generative Adversarial Network for Text Generation

• URL: http://arxiv.org/abs/2103.11578v2
• Date: Mon, 24 Jul 2023 06:53:10 GMT
• Title: SparseGAN: Sparse Generative Adversarial Network for Text Generation
• Authors: Liping Yuan, Jiehang Zeng, Xiaoqing Zheng
• Abstract summary: We propose a SparseGAN that generates semantic-interpretable, but sparse sentence representations as inputs to the discriminator. With such semantic-rich representations, we not only reduce unnecessary noises for efficient adversarial training, but also make the entire training process fully differentiable.
• Score: 8.634962333084724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is still a challenging task to learn a neural text generation model under the framework of generative adversarial networks (GANs) since the entire training process is not differentiable. The existing training strategies either suffer from unreliable gradient estimations or imprecise sentence representations. Inspired by the principle of sparse coding, we propose a SparseGAN that generates semantic-interpretable, but sparse sentence representations as inputs to the discriminator. The key idea is that we treat an embedding matrix as an over-complete dictionary, and use a linear combination of very few selected word embeddings to approximate the output feature representation of the generator at each time step. With such semantic-rich representations, we not only reduce unnecessary noises for efficient adversarial training, but also make the entire training process fully differentiable. Experiments on multiple text generation datasets yield performance improvements, especially in sequence-level metrics, such as BLEU.

Related papers

• LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network [63.554061288184165]
  We propose a novel parameterized text shape method based on low-rank approximation. By exploring the shape correlation among different text contours, our method achieves consistency, compactness, simplicity, and robustness in shape representation. We implement an accurate and efficient arbitrary-shaped text detector named LRANet.
  arXiv  Detail & Related papers  (2023-06-27T02:03:46Z)
• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)
• Variational Sparse Coding with Learned Thresholding [6.737133300781134]
  We propose a new approach to variational sparse coding that allows us to learn sparse distributions by thresholding samples. We first evaluate and analyze our method by training a linear generator, showing that it has superior performance, statistical efficiency, and gradient estimation.
  arXiv  Detail & Related papers  (2022-05-07T14:49:50Z)
• 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)
• Discovering Non-monotonic Autoregressive Orderings with Variational Inference [67.27561153666211]
  We develop an unsupervised parallelizable learner that discovers high-quality generation orders purely from training data. We implement the encoder as a Transformer with non-causal attention that outputs permutations in one forward pass. Empirical results in language modeling tasks demonstrate that our method is context-aware and discovers orderings that are competitive with or even better than fixed orders.
  arXiv  Detail & Related papers  (2021-10-27T16:08:09Z)
• Interpreting intermediate convolutional layers in unsupervised acoustic word classification [0.0]
  This paper proposes a technique to visualize and interpret intermediate layers of unsupervised deep convolutional neural networks. A GAN-based architecture (ciwGAN arXiv:2006.02951) was trained on unlabeled sliced lexical items from TIMIT.
  arXiv  Detail & Related papers  (2021-10-05T21:53:32Z)
• Alleviate Exposure Bias in Sequence Prediction \\ with Recurrent Neural Networks [47.52214243454995]
  A popular strategy to train recurrent neural networks (RNNs) is to take the ground truth as input at each time step. We propose a fully differentiable training algorithm for RNNs to better capture long-term dependencies.
  arXiv  Detail & Related papers  (2021-03-22T06:15:22Z)
• The Interpretable Dictionary in Sparse Coding [4.205692673448206]
  In our work, we illustrate that an ANN, trained using sparse coding under specific sparsity constraints, yields a more interpretable model than the standard deep learning model. The dictionary learned by sparse coding can be more easily understood and the activations of these elements creates a selective feature output.
  arXiv  Detail & Related papers  (2020-11-24T00:26:40Z)
• Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference [59.62779187457773]
  We propose a generative classifier for natural language inference (NLI) We compare it to five baselines, including discriminative models and large-scale pretrained language representation models like BERT. Experiments show that GenNLI outperforms both discriminative and pretrained baselines across several challenging NLI experimental settings.
  arXiv  Detail & Related papers  (2020-10-08T04:44:00Z)
• POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training [93.79766670391618]
  We present POINTER, a novel insertion-based approach for hard-constrained text generation. The proposed method operates by progressively inserting new tokens between existing tokens in a parallel manner. The resulting coarse-to-fine hierarchy makes the generation process intuitive and interpretable.
  arXiv  Detail & Related papers  (2020-05-01T18:11:54Z)
• 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)

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.