| Challenge: | Compared with string-to-string systems, tree-based NMT methods use more syntactic information and can incorporate prior knowledge. |
| Approach: | They propose a tree-based neural machine translation method that translates a linearized packed forest under a simple sequence-to-sequence framework. |
| Outcome: | The proposed method outperforms tree-based approaches in the BLEU score of the proposed model. |
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Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model (C18-1)
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| Challenge: | Incorporating syntactic information in machine translation can lead to better reorderings, especially useful when the language pairs are syntaktically highly divergent. |
| Approach: | They propose a forest-to-sequence NMT model which uses exponentially many parse trees of the source sentence to compensate for parser errors. |
| Outcome: | The proposed model outperforms the sequence-to-sequence and tree-to tree-based models on English, Chinese and Farsi translation tasks. |
A Tree-based Decoder for Neural Machine Translation (D18-1)
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| Challenge: | Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures. |
| Approach: | They propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side. |
| Outcome: | The proposed model outperforms standard seq2seq models by 2.1 BLEU points and other methods for incorporating target-side syntax by 0.7 BLUE points. |
Deconvolution-Based Global Decoding for Neural Machine Translation (C18-1)
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| Challenge: | Existing models for Neural Machine Translation (NMT) use Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. |
| Approach: | They propose a Neural Machine Translation (NMT) model that decodes the sequence with the guidance of its structural prediction of the target-side context. |
| Outcome: | The proposed model is more competitive compared with the state-of-the-art methods and reduces repetition with the instruction from the target-side context for decoding. |
Improving Neural Machine Translation with Neural Syntactic Distance (N19-1)
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| Challenge: | Neural syntactic distance (NSD) is used to represent constituent trees using a sequence whose length is identical to the number of words in the sentence. |
| Approach: | They propose five strategies to improve NMT with explicit use of syntactic information . et al., 2014) propose a set of five strategies that incorporate syntastic information into the encoder and/or decoder of the baseline model. |
| Outcome: | The proposed strategies improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En-Ch), and +1.0 (Ch–En) BLEU. |
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing (D18-1)
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| Challenge: | Neural machine translation (NMT) models are based on sequential decoding or serialisation of structured data into sequence. |
| Approach: | They propose a model that combines sequential encoder with tree-structured decoding augmented with a syntax-aware attention model. |
| Outcome: | The proposed model produces fluent translations with better reordering than previous models. |
Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
| Approach: | They propose an unsupervised approach which trains the model without labeling data . they propose two independent encoders but share some partial weights to extract high-level representations of input sentences. |
| Outcome: | The proposed approach achieves significant improvements on English-German, English-French and Chinese-to-English translation tasks. |
A Survey of Domain Adaptation for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
| Approach: | They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available. |
| Outcome: | The proposed approach yields the state-of-the-art translation performance in resource rich scenarios. |
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations (N19-1)
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| Challenge: | Syntax integration has been demonstrated highly effective in neural machine translation (NMT). |
| Approach: | They propose a method to integrate source-side syntax implicitly for neural machine translation . they use hidden representations of a well-trained end-to-end dependency parser to concatenate them with ordinary word embeddings to enhance basic NMT models. |
| Outcome: | The proposed method outperforms existing methods on two translation tasks . it can be easily integrated into the widely-used sequence-to-sequence (Seq2Sequen) framework . |
An Effective Approach to Unsupervised Machine Translation (P19-1)
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| Challenge: | a recent research line has managed to train both unsupervised and unsupervised machine translation systems using monolingual corpora only. |
| Approach: | They propose to use monolingual corpora to train both unsupervised and unsupervised machine translation systems. |
| Outcome: | The proposed system achieves 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more in the (supervised) shared task winner back in 2014. |
Improving Neural Machine Translation with Soft Template Prediction (2020.acl-main)
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| Challenge: | Recent advances in neural machine translation (NMT) depend on source text to generate translation. |
| Approach: | They propose to use extracted templates from tree structures as soft target templates to guide the translation procedure. |
| Outcome: | The proposed model outperforms baseline models on four benchmarks and demonstrates the effectiveness of soft target templates. |