We lifted 14 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ringos/nfc-parser | pwc_unofficial | 3 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| collapse_unary_strip_pos | Ran | ringos/nfc-parser/src/analysis/get_sibling.py code served (permissive licence) · get_code("bd029ebdc4c2a411") |
| format_elapsed | Ran | ringos/nfc-parser/src/export.py code served (permissive licence) · get_code("d568621c7cfd96cc") |
| pad_charts | Ran | ringos/nfc-parser/src/benepar/decode_chart.py code served (permissive licence) · get_code("5fa1c233f4e619bb") |
| arabic | Not yet run | ringos/nfc-parser/src/transliterate.py code served (permissive licence) · get_code("c6fbc9f36f128a4d") |
| evalb | Not yet run | ringos/nfc-parser/src/evaluate.py code served (permissive licence) · get_code("7186374092dc5b49") |
| get_labeled_spans | Not yet run | ringos/nfc-parser/src/analysis/get_sibling.py code served (permissive licence) · get_code("847edf90713bade6") |
| get_labeled_spans | Not yet run | ringos/nfc-parser/src/benepar/decode_chart.py code served (permissive licence) · get_code("7f1d42a1ee822236") |
| get_multi_ngram_pattern_children | Not yet run | ringos/nfc-parser/src/analysis/get_pattern_constituent_pair.py code served (permissive licence) · get_code("31b6275073ba81d5") |
| get_pattern_children | Not yet run | ringos/nfc-parser/src/analysis/get_pattern_constituent_pair.py code served (permissive licence) · get_code("59e93379f77df089") |
| hebrew | Not yet run | ringos/nfc-parser/src/transliterate.py code served (permissive licence) · get_code("98db1b220dd2ecb7") |
| load_compatitble_data | Not yet run | ringos/nfc-parser/src/analysis/get_pattern_constituent_pair.py code served (permissive licence) · get_code("2ed97e38f7090303") |
| load_trees | Not yet run | ringos/nfc-parser/src/analysis/trees.py code served (permissive licence) · get_code("79f8c62f9471a066") |
| load_trees_from_text | Not yet run | ringos/nfc-parser/src/analysis/trees.py code served (permissive licence) · get_code("7392b7fad2fd08a6") |
| tree_from_str | Not yet run | ringos/nfc-parser/src/analysis/trees.py code served (permissive licence) · get_code("1be51ebb6a7c65f5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-of-the-art discriminative constituency parser. The use of attention makes explicit the manner in which information is propagated between different locations in the sentence, which we use to both analyze our model and propose potential improvements. For example, we find that separating positional and content information in the encoder can lead to improved parsing accuracy. Additionally, we evaluate different approaches for lexical representation. Our parser achieves new state-of-the-art results for single models trained on the Penn Treebank: 93.55 F1 without the use of any external data, and 95.13 F1 when using pre-trained word representations. Our parser also outperforms the previous best-published accuracy figures on 8 of the 9 languages in the SPMRL dataset.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1805.01052")
get_code_for_paper("1805.01052")
have("1805.01052")
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