Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention

SEMEVAL 2017  ·  Jan Buys, Phil Blunsom ·

We present a neural encoder-decoder AMR parser that extends an attention-based model by predicting the alignment between graph nodes and sentence tokens explicitly with a pointer mechanism. Candidate lemmas are predicted as a pre-processing step so that the lemmas of lexical concepts, as well as constant strings, are factored out of the graph linearization and recovered through the predicted alignments. The approach does not rely on syntactic parses or extensive external resources. Our parser obtained 59{\%} Smatch on the SemEval test set.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
AMR Parsing LDC2017T10 Neural-Pointer Smatch 61.9 # 27


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