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.

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Challenge: Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures.
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Challenge: Discourse representation tree structure (DRTS) parsing is a new semantic parser which ignores structural information.
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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.
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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).
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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.
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Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020.findings-emnlp)

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Challenge: Graph2Tree model encodes graph-structured input and decodes tree-structures output.
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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.
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Challenge: Recent advances in neural machine translation (NMT) depend on source text to generate translation.
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Multi-Source Syntactic Neural Machine Translation (D18-1)

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Challenge: Existing approaches to integrate source syntax into neural machine translations use linearized parses.
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