CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks

We describe the system used in our participation in the AMR Parsing task for SemEval-2016. Our parser does not rely on a syntactic pre-parse, or heavily engineered features, and uses ﬁve recurrent neural networks as the key architectural components for estimating AMR graph structure

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CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks

Semantic Scholar · Computer Science · 2016

Abstract

We describe the system used in our participation in the AMR Parsing task for SemEval-2016. Our parser does not rely on a syntactic pre-parse, or heavily engineered features, and uses ﬁve recurrent neural networks as the key architectural components for estimating AMR graph structure

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