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- W2738955616 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." @default.
- W2738955616 created "2017-07-31" @default.
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- W2738955616 date "2017-07-24" @default.
- W2738955616 modified "2023-09-27" @default.
- W2738955616 title "AMR Parsing using Stack-LSTMs" @default.
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