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AMR-to-text generation as a Traveling Salesman Problem
September 23, 2016 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Linfeng Song, Yue Zhang, Xiaochang Peng, Zhiguo Wang, Daniel Gildea
arXiv ID
1609.07451
Category
cs.CL: Computation & Language
Citations
34
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
4 months ago
Abstract
The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR graph into smaller fragments, and then generating the translation for each fragment, before finally deciding the order by solving an asymmetric generalized traveling salesman problem (AGTSP). A Maximum Entropy classifier is trained to estimate the traveling costs, and a TSP solver is used to find the optimized solution. The final model reports a BLEU score of 22.44 on the SemEval-2016 Task8 dataset.
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