Papers with explicit Tree-RNN
Transition-based Neural RST Parsing with Implicit Syntax Features (C18-1)
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| Challenge: | Syntax has been a useful source of information for statistical RST discourse parsing. |
| Approach: | They propose an implicit syntax feature extraction approach using hidden-layer vectors extracted from a neural syntax parser. |
| Outcome: | The proposed model with dynamic oracle is competitive with existing models. |
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 . |