AMR Parsing Using Stack-lstms
Miguel Ballesteros, Yaser Al-Onaizan . Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing 2017 – 74 citations
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.