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21mBprZ3au

https://paperswithcode.com/paper/the-variational-fair-autoencoder

The Variational Fair Autoencoder

We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible. Our model is based on a variational autoencoding architecture with priors that encourage independence between sensitive and latent factors of variation. Any subsequent processing, such as classification, can then be performed on this purged latent representation. To remove any remaining dependencies we incorporate an additional penalty term based on the "Maximum Mean Discrepancy" (MMD) measure. We discuss how these architectures can be efficiently trained on data and show in experiments that this method is more effective than previous work in removing unwanted sources of variation while maintaining informative latent representations.

1511.00830

http://arxiv.org/abs/1511.00830v6

http://arxiv.org/pdf/1511.00830v6.pdf

mzmZPxHbHZ

https://paperswithcode.com/paper/breaking-the-softmax-bottleneck-a-high-rank

Breaking the Softmax Bottleneck: A High-Rank RNN Language Model

We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language. We propose a simple and effective method to address this issue, and improve the state-of-the-art perplexities on Penn Treebank and WikiText-2 to 47.69 and 40.68 respectively. The proposed method also excels on the large-scale 1B Word dataset, outperforming the baseline by over 5.6 points in perplexity.

1711.03953

http://arxiv.org/abs/1711.03953v4

http://arxiv.org/pdf/1711.03953v4.pdf

4sgwBMIVZJ

https://paperswithcode.com/paper/partially-shuffling-the-training-data-to-1

Partially Shuffling the Training Data to Improve Language Models

Although SGD requires shuffling the training data between epochs, currently none of the word-level language modeling systems do this. Naively shuffling all sentences in the training data would not permit the model to learn inter-sentence dependencies. Here we present a method that partially shuffles the training data between epochs. This method makes each batch random, while keeping most sentence ordering intact. It achieves new state of the art results on word-level language modeling on both the Penn Treebank and WikiText-2 datasets.

1903.04167

http://arxiv.org/abs/1903.04167v2

http://arxiv.org/pdf/1903.04167v2.pdf

wjL-ZZVuIm

https://paperswithcode.com/paper/dynamic-evaluation-of-neural-sequence-models

Dynamic Evaluation of Neural Sequence Models

We present methodology for using dynamic evaluation to improve neural sequence models. Models are adapted to recent history via a gradient descent based mechanism, causing them to assign higher probabilities to re-occurring sequential patterns. Dynamic evaluation outperforms existing adaptation approaches in our comparisons. Dynamic evaluation improves the state-of-the-art word-level perplexities on the Penn Treebank and WikiText-2 datasets to 51.1 and 44.3 respectively, and the state-of-the-art character-level cross-entropies on the text8 and Hutter Prize datasets to 1.19 bits/char and 1.08 bits/char respectively.

1709.07432

http://arxiv.org/abs/1709.07432v2

http://arxiv.org/pdf/1709.07432v2.pdf

Afw7UcYbWU

https://paperswithcode.com/paper/direct-output-connection-for-a-high-rank

Direct Output Connection for a High-Rank Language Model

This paper proposes a state-of-the-art recurrent neural network (RNN) language model that combines probability distributions computed not only from a final RNN layer but also from middle layers. Our proposed method raises the expressive power of a language model based on the matrix factorization interpretation of language modeling introduced by Yang et al. (2018). The proposed method improves the current state-of-the-art language model and achieves the best score on the Penn Treebank and WikiText-2, which are the standard benchmark datasets. Moreover, we indicate our proposed method contributes to two application tasks: machine translation and headline generation. Our code is publicly available at: https://github.com/nttcslab-nlp/doc_lm.

1808.10143

http://arxiv.org/abs/1808.10143v2

http://arxiv.org/pdf/1808.10143v2.pdf

nCrJQdu1BQ

https://paperswithcode.com/paper/on-the-state-of-the-art-of-evaluation-in

On the State of the Art of Evaluation in Neural Language Models

Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing code bases and limited computational resources, which represent uncontrolled sources of experimental variation. We reevaluate several popular architectures and regularisation methods with large-scale automatic black-box hyperparameter tuning and arrive at the somewhat surprising conclusion that standard LSTM architectures, when properly regularised, outperform more recent models. We establish a new state of the art on the Penn Treebank and Wikitext-2 corpora, as well as strong baselines on the Hutter Prize dataset.

1707.05589

http://arxiv.org/abs/1707.05589v2

http://arxiv.org/pdf/1707.05589v2.pdf