Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Code

• github.com/rvandewater/yaib
  pytorch★ 94
• github.com/ratschlab/HIRID-ICU-Benchmark
  pytorch★ 60
• github.com/Nick-Zhao-Engr/Machine-Translation
  pytorch★ 17
• github.com/advaithsrao/Chatbot_Seq2seq_model
  tf★ 1
• github.com/astorfi/sequence-to-sequence-from-scratch
  pytorch★ 0
• github.com/farizrahman4u/seq2seq
  none★ 0
• github.com/spratskevich/Lemmatizer
  none★ 0
• github.com/littleflow3r/Sequence_to_sequence_learning_with_NN
  pytorch★ 0
• github.com/dksifoua/Neural-Machine-Translation
  pytorch★ 0
• github.com/littleflow3r/Sequence_to_sequence_learning_for_machine_translation
  pytorch★ 0

Abstract

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.

Tasks

Benchmark Results

Reproductions