Sebastian Ruder, Sebastian Riedel, Pontus Stenetorp, Marcin Kardas, Robert Stojnic, Ross Taylor, Piotr Czapla
We lifted 14 functions out of this paper's own repositories and ran 12 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 |
|---|---|---|
| paperswithcode/axcell | canonical | 12 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| annotations | Ran | paperswithcode/axcell/axcell/helpers/optimize.py code served (permissive licence) · get_code("53bc9344cc348810") |
| estimate_noises | Ran | paperswithcode/axcell/axcell/helpers/optimize.py code served (permissive licence) · get_code("3387a9ba01a1d9a2") |
| load_references | Ran | paperswithcode/axcell/axcell/helpers/cache.py code served (permissive licence) · get_code("83f38dae7103f51b") |
| load_structure | Ran | paperswithcode/axcell/axcell/helpers/cache.py code served (permissive licence) · get_code("ad584250d25d1fb1") |
| load_tags | Ran | paperswithcode/axcell/axcell/helpers/cache.py code served (permissive licence) · get_code("e150c098756c6b8e") |
| norm_score_str | Ran | paperswithcode/axcell/axcell/helpers/evaluate.py code served (permissive licence) · get_code("b75165430b90d460") |
| precision | Ran | paperswithcode/axcell/axcell/helpers/evaluate.py code served (permissive licence) · get_code("7fc6d3a39db0d9de") |
| read_arxiv_papers | Ran | paperswithcode/axcell/axcell/helpers/datasets.py code served (permissive licence) · get_code("4360ff7024678437") |
| read_tables_annotations | Ran | paperswithcode/axcell/axcell/helpers/datasets.py code served (permissive licence) · get_code("02b6a005a3d5a4bb") |
| recall | Ran | paperswithcode/axcell/axcell/helpers/evaluate.py code served (permissive licence) · get_code("f02f73aa554eecb3") |
| ro_bind | Ran | paperswithcode/axcell/axcell/helpers/latex_converter.py code served (permissive licence) · get_code("f8b1fe756d77cd7e") |
| rw_bind | Ran | paperswithcode/axcell/axcell/helpers/latex_converter.py code served (permissive licence) · get_code("f05aaf2be87524df") |
| estimate_context_noise | Not yet run | paperswithcode/axcell/axcell/helpers/optimize.py code served (permissive licence) · get_code("86fb74ea9103b92b") |
| table_to_html | Not yet run | paperswithcode/axcell/axcell/helpers/jupyter.py code served (permissive licence) · get_code("4767e70065da93cd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AXCELL, an automatic machine learning pipeline for extracting results from papers. AXCELL uses several novel components, including a table segmentation subtask, to learn relevant structural knowledge that aids extraction. When compared with existing methods, our approach significantly improves the state of the art for results extraction. We also release a structured, annotated dataset for training models for results extraction, and a dataset for evaluating the performance of models on this task. Lastly, we show the viability of our approach enables it to be used for semi-automated results extraction in production, suggesting our improvements make this task practically viable for the first time. Code is available on GitHub. 1 Back-translation . . .
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2004.14356")
get_code_for_paper("2004.14356")
have("2004.14356")
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