Papers › Linguistic Information in Neural Semantic Parsing with Multiple Encoders
Linguistic Information in Neural Semantic Parsing with Multiple Encoders
Rik van Noord, Antonio Toral, Johan Bos
Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks. However, they often do not exploit available linguistic resources, while these, when employed correctly, are likely to increase performance even further. Research in neural machine translation has shown that employing this information has a lot of potential, especially when using a multi-encoder setup. We employ a range of semantic and syntactic resources to improve performance for the task of Discourse Representation Structure Parsing. We show that (i) linguistic features can be beneficial for neural semantic parsing and (ii) the best method of adding these features is by using multiple encoders.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| DRS Parsing | PMB-2.2.0 | Character-level bi-LSTM seq2seq + linguistic features | F1 | 86.8 | #3 of 6 | Archive leaderboard | report |
| DRS Parsing | PMB-3.0.0 | Character-level bi-LSTM seq2seq + linguistic features | F1 | 87.7 | #2 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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