Papers › Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT
Rik van Noord, Antonio Toral, Johan Bos
We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. Character representations can easily be added in a sequence-to-sequence model in either one encoder or as a fully separate encoder, with improvements that are robust to different language models, languages and data sets. For English, these improvements are larger than adding individual sources of linguistic information or adding non-contextual embeddings. A new method of analysis based on semantic tags demonstrates that the character-level representations improve performance across a subset of selected semantic phenomena.
Code
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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 | Bi-LSTM seq2seq: BERT + characters in 1 encoder | F1 | 88.3 | #1 of 6 | Archive leaderboard | report |
| DRS Parsing | PMB-3.0.0 | Bi-LSTM seq2seq: BERT + characters in 1 encoder | F1 | 89.3 | #1 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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