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[1804.07915] A Stable and Effective Learning Strategy for Trainable Greedy Decoding
Beam search is a widely used approximate search strategy for neural network decoders, and it generally outperforms simple greedy decoding on tasks like machine translation. However, this improvement comes at substantial computational cost. In this paper, we propose a flexible new method that allows us to reap nearly the full benefits of beam search with nearly no additional computational cost. The method revolves around a small neural network actor that is trained to observe and manipulate the hidden state of a previously-trained decoder. To train this actor network, we introduce the use of a pseudo-parallel corpus built using the output of beam search on a base model, ranked by a target quality metric like BLEU. Our method is inspired by earlier work on this problem, but requires no reinforcement learning, and can be trained reliably on a range of models. Experiments on three parallel corpora and three architectures show that the method yields substantial improvements in translation quality and speed over each base system.
seq2seq  rnn  decoding  beam-search  via:chl 
5 weeks ago by arsyed
dreasysnail/textCNN_public
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 85 million projects.
tensorflow  convolutional  seq2seq  autoencoder  deep-learning  github 
july 2018 by nharbour
ymym3412/textcnn-conv-deconv-pytorch: text convolution-deconvolution auto-encoder model in PyTorch
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 85 million projects.
pytorch  convolutional  seq2seq  autoencoder  deep-learning  github 
july 2018 by nharbour
Seq2Seq-PyTorch/nmt_autoencoder.py at master · MaximumEntropy/Seq2Seq-PyTorch
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 85 million projects.
autoencoder  pytorch  nlp  lstm  deep-learning  seq2seq 
july 2018 by nharbour

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