# AMR Parsing using Stack-LSTMs

Source: [AMR Parsing using Stack-LSTMs](<https://www.sota2.com/research/paper/68fa638a1e209131ce9ff67e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-14T16:48:31.557Z

Authors: Miguel Ballesteros, Yaser Al-Onaizan

Published: 2017-07-24T21:33:21.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [AMR parsing on LDC2014T12 Newswire section](<https://www.sota2.com/research/sota/amr-parsing-on-ldc2014t12-newswire-section.md>): AMR parsing; LDC2014T12 Newswire section; F1 Score: 69.

- [AMR Parsing on LDC2014T12 (Full)](<https://www.sota2.com/research/sota/amr-parsing-on-ldc2014t12-full.md>): AMR parsing; LDC2014T12 (Full); F1 Score: 64.

- [AMR Parsing on LDC2017T10 AMR 2.0 (test)](<https://www.sota2.com/research/sota/amr-parsing-on-ldc2017t10-amr-2-0-test.md>): AMR parsing; LDC2017T10 AMR 2.0 (test); Smatch: 65.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1707.07755>)

[PDF](<https://arxiv.org/pdf/1707.07755.pdf>)