Bag-of-Words as Target for Neural Machine Translation
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Summary
This paper proposes an approach that uses both the sentences and the bag-of-words as targets in the training stage, in order to encourage the model to generate the potentially correct sentences that are not appeared in theTraining set.
- Type
- preprint
- Published
- 2018-05-01
- Cited by
- 78
- References
- 34
- Access
- Open access
- OpenAlex
- https://openalex.org/W2799090016
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:44108216
Keywords
Computer science, Machine translation, Sentence, Natural language processing, Artificial intelligence
References
- Recurrent Continuous Translation Models
- On the difficulty of training recurrent neural networks
- Effective Approaches to Attention-based Neural Machine Translation
- On Using Very Large Target Vocabulary for Neural Machine Translation
- Bleu: a Method for Automatic Evaluation of Machine Translation
- Posterior Regularization for Structured Latent Variable Models
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- Neural Summarization by Extracting Sentences and Words
- Supervised Attentions for Neural Machine Translation
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation
- Neural Headline Generation on Abstract Meaning Representation
- Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation
- Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
- Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
- Convolutional Sequence to Sequence Learning
- Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder
- Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
- Multi-channel Encoder for Neural Machine Translation
- Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation
Cited by
- Training Simplification and Model Simplification for Deep Learning : A Minimal Effort Back Propagation Method
- DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text
- Automatic Transferring between Ancient Chinese and Contemporary Chinese
- Live Video Comment Generation Based on Surrounding Frames and Live Comments
- Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation
- An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
- A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
- Exchange-Based Diffusion in Hb-Graphs: Highlighting Complex Relationships
- Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation
- Future-Prediction-Based Model for Neural Machine Translation
- Glyce: Glyph-vectors for Chinese Character Representations
- Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation
- ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems
- Identifying extra high frequency gravitational waves generated from oscillons with cuspy potentials using deep neural networks
- Is Word Segmentation Necessary for Deep Learning of Chinese Representations?
- Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation
- Generating Sentences from Disentangled Syntactic and Semantic Spaces
- Deconvolution-Based Global Decoding for Neural Machine Translation
- Paraphrase Generation with Latent Bag of Words
- PubMedQA: A Dataset for Biomedical Research Question Answering
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