Publication
EMNLP 2017
Conference paper

AMR parsing using stack-LSTMs

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Abstract

We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data. Adding additional information, such as POS tags and dependency trees, improves the results further.

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Publication

EMNLP 2017

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