Tree Decomposition Attention for AMR-to-Text Generation
This work improves AMR-to-text generation for natural language processing applications, representing an incremental advancement with specific gains.
The paper tackled the problem of generating text from Abstract Meaning Representation (AMR) graphs by addressing poor vertex dependency capture in Transformer-based encoders, resulting in a system that outperforms a self-attentive baseline by 1.6 BLEU and 1.8 chrF++ scores.
Text generation from AMR requires mapping a semantic graph to a string that it annotates. Transformer-based graph encoders, however, poorly capture vertex dependencies that may benefit sequence prediction. To impose order on an encoder, we locally constrain vertex self-attention using a graph's tree decomposition. Instead of forming a full query-key bipartite graph, we restrict attention to vertices in parent, subtree, and same-depth bags of a vertex. This hierarchical context lends both sparsity and structure to vertex state updates. We apply dynamic programming to derive a forest of tree decompositions, choosing the most structurally similar tree to the AMR. Our system outperforms a self-attentive baseline by 1.6 BLEU and 1.8 chrF++.