Towards Neural Phrase-based Machine Translation

By Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng. 2017

66 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Tan
2. Mohit Bansal
3. Yoshua Bengio
4. Graham Neubig
5. Zhe Gan
6. Kyunghyun Cho
7. Tao Qin
8. Dacheng Tao

Citations in the corpus

• Classical Structured Prediction Losses for Sequence to Sequence Learning (2017, background): “We also show the likelihood baseline (MLE) of Wiseman and Rush (2016), their beam search optimization method (BSO), the actor critic result of Bahdanau et al. (2016) as well as the best reported result on this dataset to date by Huang et al. (2017).”
• Contextual Parameter Generation for Universal Neural Machine Translation (2018, positive): “Huang et al. (2018) The presented results provide evidence that our proposed approach is able to significantly improve performance, without requiring extensive tuning.”
• Neural Machine Translation: A Review (2019, unknown): “ngleinputtokensince theencoderhiddenstatesarebythemselvescontext-sensitive. 42 Figure20: Neuralnetworkswithexternalmemory. studiedbyRiktersandBojar[408],Ishiwatarietal.[409],Fengetal.[410],Huangetal. [411],Lietal.[412],Eriguchietal.[413]. Ontheothersideofthespectrum,ithasbeennotedthatregularattentio”
• Benchmarking Hierarchical Script Knowledge (2019, background): “Closely related architectures have been proposed for segmental sequence modeling (Wang et al., 2017) and phrase-based neural machine translation (Huang et al., 2018).”
• Revisiting Low-Resource Neural Machine Translation: A Case Study (2019, methodology): “…that were optimized to the ultra-low data condition (8) system BLEU MIXER (Ranzato et al., 2016) 5 21.8 BSO (Wiseman and Rush, 2016) 25.5 NPMT+LM (Huang et al., 2018) 30.1 MRT (Edunov et al., 2018) 32.84 ± 0.08 Pervasive Attention (Elbayad et al., 2018) 33.8 Transformer Baseline (Wu et al.,…”
• Syntax-Aware Action Targeting for Video Captioning (2020, background): “The role of syntax components, which are considered to contain more semantic information, has been emphasized in sentence/text generation [22, 49, 7, 21].”
• Sequence-to-Sequence Learning with Latent Neural Grammars (2021, unknown): “Since we marginalize over unobserved trees during learning, our work is also related to the line of work on marginalizing out latent variables/structures for sequence transduction tasks [46, 33, 11, 66, 128, 90, 57, 69, 108, 111, inter alia ].”
• Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP (2021, result): “13Interestingly, with some reordering of the input one can break monotonicity between input and output, making the model similar to phrase-based MT (Huang et al., 2018).”
• Sentence-wise Smooth Regularization for Sequence to Sequence Learning (2018, methodology): “We use the concatenation of dev2010, tst2010, tst2011 and tst2011 as the test set, which is widely adopted in (Huang et al. 2017; Bahdanau et al. 2016).”
• Efficient Bidirectional Neural Machine Translation (2019, methodology): “Following the common practice (Ranzato et al., 2015; Bahdanau et al., 2017; Huang et al., 2017), the test set is the concatenation of dev2010, tst2010, tst2011 and tst2012, and all the tokens in the training/validation/test sentences are lower-cased.”
• Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction (2018, unknown): “De-En Flops ( × 10 5 ) # prms En-De # prms Conv-LSTM (MLE) (Bahdanau et al., 2017) 27.56 Bi-GRU (MLE+SLE) (Bahdanau et al., 2017) 28.53 Conv-LSTM (deep+pos) (Gehring et al., 2017a) 30.4 NPMT + language model (Huang et al., 2018) 30.08 25.36”
• Dense Information Flow for Neural Machine Translation (2018, methodology): “As shown in Table 4, DenseNMT outperforms the phrasestructure based network NPMT (Huang et al., 2017) (with beam size 10) by 1.2 BLEU, using a smaller beam size, and outperforms the actorcritic method based algorithm (Bahdanau et al., 2017) by 2.8 BLEU.”
• Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search (2018, methodology): “Appendix A contains examples of words that most strongly Model DE → EN BLEU EN → DE BLEU MIXER (Ranzato et al., 2016) 21.8 Beam Search Optimization (Wiseman and Rush, 2016) 25.5 Actor-Critic + Log Likelihood (Bahdanau et al., 2017) 28.5 Neural Phrase-based Machine Translation (Huang et al., 2018a) 2”
• Learning Neural Templates for Text Generation (2018, methodology): “…variables in this context, including extracting representations (Shen et al., 2018), incorporating discrete latent variables in text modeling (Yang et al., 2018), and using non-HSMM segmental models for machine translation or summarization (Yu et al., 2016; Wang et al., 2017; Huang et al., 2018).”
• Multi-layer Representation Fusion for Neural Machine Translation (2018, methodology): “A good instance is DenseNet that applies a fully-connected convolutional network to computer vision (Huang et al., 2017a).”