Papers › AMR Parsing using Stack-LSTMs
AMR Parsing using Stack-LSTMs
Miguel Ballesteros, Yaser Al-Onaizan
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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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| AMR Parsing | LDC2014T12 | Transition-based parser-Stack-LSTM | F1 Full | 63 | #11 of 12 | Archive leaderboard | report |
| AMR Parsing | LDC2014T12 | Transition-based parser-Stack-LSTM | F1 Newswire | 68 | #11 of 12 | Archive leaderboard | report |
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