The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

Zichao Yang - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised text style transfer using language models as Discriminators
    Neural Information Processing Systems, 2018
    Co-Authors: Zichao Yang, Chris Dyer, Eric Po Xing, Zhiting Hu, Taylor Bergkirkpatrick
    Abstract:

    Binary classifiers are employed as Discriminators in GAN-based unsupervised style transfer models to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with the binary discriminator is that error signal is sometimes insufficient to train the model to produce rich-structured language. In this paper, we propose a technique of using a target domain language model as the discriminator to provide richer, token-level feedback during the learning process. Because our language model scores sentences directly using a product of locally normalized probabilities, it offers more stable and more useful training signal to the generator. We train the generator to minimize the negative log likelihood (NLL) of generated sentences evaluated by a language model. By using continuous approximation of the discrete samples, our model can be trained using back-propagation in an end-to-end way. Moreover, we find empirically with a language model as a structured discriminator, it is possible to eliminate the adversarial training steps using negative samples, thus making training more stable. We compare our model with previous work using convolutional neural networks (CNNs) as Discriminators and show our model outperforms them significantly in three tasks including word substitution decipherment, sentiment modification and related language translation.

  • Unsupervised text style transfer using language models as Discriminators
    Advances in Neural Information Processing Systems, 2018
    Co-Authors: Zichao Yang, Chris Dyer, Eric Po Xing, Zhiting Hu, Taylor Berg-kirkpatrick
    Abstract:

    Binary classifiers are often employed as Discriminators in GAN-based unsupervised style transfer systems to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with this approach is that the error signal provided by the discriminator can be unstable and is sometimes insufficient to train the generator to produce fluent language. In this paper, we propose a new technique that uses a target domain language model as the discriminator, providing richer and more stable token-level feedback during the learning process. We train the generator to minimize the negative log likelihood (NLL) of generated sentences, evaluated by the language model. By using a continuous approximation of discrete sampling under the generator, our model can be trained using back-propagation in an end- to-end fashion. Moreover, our empirical results show that when using a language model as a structured discriminator, it is possible to forgo adversarial steps during training, making the process more stable. We compare our model with previous work using convolutional neural networks (CNNs) as Discriminators and show that our approach leads to improved performance on three tasks: word substitution decipherment, sentiment modification, and related language translation.

Taylor Bergkirkpatrick - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised text style transfer using language models as Discriminators
    Neural Information Processing Systems, 2018
    Co-Authors: Zichao Yang, Chris Dyer, Eric Po Xing, Zhiting Hu, Taylor Bergkirkpatrick
    Abstract:

    Binary classifiers are employed as Discriminators in GAN-based unsupervised style transfer models to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with the binary discriminator is that error signal is sometimes insufficient to train the model to produce rich-structured language. In this paper, we propose a technique of using a target domain language model as the discriminator to provide richer, token-level feedback during the learning process. Because our language model scores sentences directly using a product of locally normalized probabilities, it offers more stable and more useful training signal to the generator. We train the generator to minimize the negative log likelihood (NLL) of generated sentences evaluated by a language model. By using continuous approximation of the discrete samples, our model can be trained using back-propagation in an end-to-end way. Moreover, we find empirically with a language model as a structured discriminator, it is possible to eliminate the adversarial training steps using negative samples, thus making training more stable. We compare our model with previous work using convolutional neural networks (CNNs) as Discriminators and show our model outperforms them significantly in three tasks including word substitution decipherment, sentiment modification and related language translation.

Taylor Berg-kirkpatrick - One of the best experts on this subject based on the ideXlab platform.

  • Unsupervised text style transfer using language models as Discriminators
    Advances in Neural Information Processing Systems, 2018
    Co-Authors: Zichao Yang, Chris Dyer, Eric Po Xing, Zhiting Hu, Taylor Berg-kirkpatrick
    Abstract:

    Binary classifiers are often employed as Discriminators in GAN-based unsupervised style transfer systems to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with this approach is that the error signal provided by the discriminator can be unstable and is sometimes insufficient to train the generator to produce fluent language. In this paper, we propose a new technique that uses a target domain language model as the discriminator, providing richer and more stable token-level feedback during the learning process. We train the generator to minimize the negative log likelihood (NLL) of generated sentences, evaluated by the language model. By using a continuous approximation of discrete sampling under the generator, our model can be trained using back-propagation in an end- to-end fashion. Moreover, our empirical results show that when using a language model as a structured discriminator, it is possible to forgo adversarial steps during training, making the process more stable. We compare our model with previous work using convolutional neural networks (CNNs) as Discriminators and show that our approach leads to improved performance on three tasks: word substitution decipherment, sentiment modification, and related language translation.

Eric Po Xing - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised text style transfer using language models as Discriminators
    Neural Information Processing Systems, 2018
    Co-Authors: Zichao Yang, Chris Dyer, Eric Po Xing, Zhiting Hu, Taylor Bergkirkpatrick
    Abstract:

    Binary classifiers are employed as Discriminators in GAN-based unsupervised style transfer models to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with the binary discriminator is that error signal is sometimes insufficient to train the model to produce rich-structured language. In this paper, we propose a technique of using a target domain language model as the discriminator to provide richer, token-level feedback during the learning process. Because our language model scores sentences directly using a product of locally normalized probabilities, it offers more stable and more useful training signal to the generator. We train the generator to minimize the negative log likelihood (NLL) of generated sentences evaluated by a language model. By using continuous approximation of the discrete samples, our model can be trained using back-propagation in an end-to-end way. Moreover, we find empirically with a language model as a structured discriminator, it is possible to eliminate the adversarial training steps using negative samples, thus making training more stable. We compare our model with previous work using convolutional neural networks (CNNs) as Discriminators and show our model outperforms them significantly in three tasks including word substitution decipherment, sentiment modification and related language translation.

  • Unsupervised text style transfer using language models as Discriminators
    Advances in Neural Information Processing Systems, 2018
    Co-Authors: Zichao Yang, Chris Dyer, Eric Po Xing, Zhiting Hu, Taylor Berg-kirkpatrick
    Abstract:

    Binary classifiers are often employed as Discriminators in GAN-based unsupervised style transfer systems to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with this approach is that the error signal provided by the discriminator can be unstable and is sometimes insufficient to train the generator to produce fluent language. In this paper, we propose a new technique that uses a target domain language model as the discriminator, providing richer and more stable token-level feedback during the learning process. We train the generator to minimize the negative log likelihood (NLL) of generated sentences, evaluated by the language model. By using a continuous approximation of discrete sampling under the generator, our model can be trained using back-propagation in an end- to-end fashion. Moreover, our empirical results show that when using a language model as a structured discriminator, it is possible to forgo adversarial steps during training, making the process more stable. We compare our model with previous work using convolutional neural networks (CNNs) as Discriminators and show that our approach leads to improved performance on three tasks: word substitution decipherment, sentiment modification, and related language translation.

Zhiting Hu - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised text style transfer using language models as Discriminators
    Neural Information Processing Systems, 2018
    Co-Authors: Zichao Yang, Chris Dyer, Eric Po Xing, Zhiting Hu, Taylor Bergkirkpatrick
    Abstract:

    Binary classifiers are employed as Discriminators in GAN-based unsupervised style transfer models to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with the binary discriminator is that error signal is sometimes insufficient to train the model to produce rich-structured language. In this paper, we propose a technique of using a target domain language model as the discriminator to provide richer, token-level feedback during the learning process. Because our language model scores sentences directly using a product of locally normalized probabilities, it offers more stable and more useful training signal to the generator. We train the generator to minimize the negative log likelihood (NLL) of generated sentences evaluated by a language model. By using continuous approximation of the discrete samples, our model can be trained using back-propagation in an end-to-end way. Moreover, we find empirically with a language model as a structured discriminator, it is possible to eliminate the adversarial training steps using negative samples, thus making training more stable. We compare our model with previous work using convolutional neural networks (CNNs) as Discriminators and show our model outperforms them significantly in three tasks including word substitution decipherment, sentiment modification and related language translation.

  • Unsupervised text style transfer using language models as Discriminators
    Advances in Neural Information Processing Systems, 2018
    Co-Authors: Zichao Yang, Chris Dyer, Eric Po Xing, Zhiting Hu, Taylor Berg-kirkpatrick
    Abstract:

    Binary classifiers are often employed as Discriminators in GAN-based unsupervised style transfer systems to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with this approach is that the error signal provided by the discriminator can be unstable and is sometimes insufficient to train the generator to produce fluent language. In this paper, we propose a new technique that uses a target domain language model as the discriminator, providing richer and more stable token-level feedback during the learning process. We train the generator to minimize the negative log likelihood (NLL) of generated sentences, evaluated by the language model. By using a continuous approximation of discrete sampling under the generator, our model can be trained using back-propagation in an end- to-end fashion. Moreover, our empirical results show that when using a language model as a structured discriminator, it is possible to forgo adversarial steps during training, making the process more stable. We compare our model with previous work using convolutional neural networks (CNNs) as Discriminators and show that our approach leads to improved performance on three tasks: word substitution decipherment, sentiment modification, and related language translation.