Papers with grammar-based approaches
Graph-to-Sequence Learning using Gated Graph Neural Networks (P18-1)
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| Challenge: | Existing approaches to graph-to-sequence learning ignore the full graph structure, discarding key information. |
| Approach: | They propose a graph-to-sequence learning model that encodes the full graph structure and an input transformation that allows nodes and edges to have their own hidden representations. |
| Outcome: | The proposed model outperforms baselines in generation from AMR graphs and syntax-based neural machine translation while retaining the full graph structure. |
Incorporating Contextual Information for Language-Independent, Dynamic Disambiguation Tasks (L18-1)
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| Challenge: | a proposed multimodal system can resolve syntactic ambiguities by exploiting external evidence, says a researcher . a parser that processes linguistic information is expected to handle syntakically unambiguous sentences, but it cannot. |
| Approach: | They propose to exploit external contextual information to resolve ambiguous sentences . they propose to use data-driven and grammar-based approaches to solve ambiguities . |
| Outcome: | The proposed system confirms this hypothesis in experiments on syntactically ambiguous sentences. |