| Challenge: | Neural machine translation models can benefit from modeling translated and untranslated source contents as recurrent states, but this less interpretable recurrence hinders their power to model dynamic updating of and contents during decoding. |
| Approach: | They propose to model the dynamic updating of and contents during decoding by explicitly separating source words into groups of translated and untranslated contents through parts-to-wholes assignment. |
| Outcome: | The proposed method achieves significant improvements over both Rnmt and Transformer by producing more adequate translations. |
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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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Towards Linear Time Neural Machine Translation with Capsule Networks (D19-1)
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| Challenge: | Neural Machine Translation (NMT) is an endto-end learning approach to machine translation. |
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Deconvolution-Based Global Decoding for Neural Machine Translation (C18-1)
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Dynamic Oracle for Neural Machine Translation in Decoding Phase (L18-1)
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| Challenge: | Existing methods to improve NMT performance but there is a discrepancy between training and inference when decoding. |
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Guiding Neural Machine Translation with Retrieved Translation Pieces (N18-1)
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| Challenge: | Neural machine translation (NMT) has trouble with lowfrequency words or phrases and generalizing across domains. |
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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. |
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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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Neural Machine Translation Decoding with Terminology Constraints (N18-2)
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