AMR Parsing using Stack-LSTMs
This work addresses semantic parsing for natural language processing, presenting an incremental improvement over existing methods.
The authors tackled AMR parsing from plain text by developing a transition-based parser using Stack-LSTMs, achieving competitive scores on English with only AMR training data and further improvements with additional linguistic features.
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.