Papers with tree encoders
Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP (2020.findings-emnlp)
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| Challenge: | Existing work on integrating syntactic information into neural networks uses a single tree, such as a constituency or a dependency tree. |
| Approach: | They propose a method to integrate heterogeneous structure knowledge into a unified sequential LSTM encoder. |
| Outcome: | The proposed method outperforms tree encoders on four syntax-dependent tasks and is efficient and accurate. |
Structural Neural Encoders for AMR-to-text Generation (N19-1)
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| Challenge: | Abstract Meaning Representation (AMR) graphs are graphs, rather than trees, because they contain reentrant nodes with multiple parents. |
| Approach: | They propose to use sequence-to-sequence models that encode AMR graphs into vector representations to generate sentences from AMRs. |
| Outcome: | The proposed model outperforms tree encoders in the AMR-to-text generation task by 24.40 points. |