Oscar Sainz, Oier Lopez De Lacalle, Eneko Agirre, Haoling Qiu, Bonan Min
We lifted 12 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| BBN-E/ZS4IE | canonical | 4 of 4 |
| osainz59/Ask2Transformers | — | 3 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| apply_threshold | Ran | osainz59/Ask2Transformers/a2t/tasks/base.py code served (permissive licence) · get_code("cf39fb09b4f7e706") |
| f1_score_ | Ran | osainz59/Ask2Transformers/a2t/tasks/base.py code served (permissive licence) · get_code("2d43ff1db5ca20c8") |
| find_optimal_threshold | Ran | osainz59/Ask2Transformers/a2t/tasks/base.py code served (permissive licence) · get_code("134e416b45e3ce29") |
| generate_entity_relation_key | Ran | BBN-E/ZS4IE/backend/a2t_service_backend.py code served (permissive licence) · get_code("7d5e6da0f3787e3a") |
| generate_mention_key | Ran | BBN-E/ZS4IE/backend/a2t_service_backend.py code served (permissive licence) · get_code("9f22a50bbb4a17d1") |
| serifxml_to_string | Ran | BBN-E/ZS4IE/backend/a2t_service_backend.py code served (permissive licence) · get_code("7be655036a05fb30") |
| sigmoid | Ran | BBN-E/ZS4IE/serif/model/entity_linker.py code served (permissive licence) · get_code("a590d62785c3fa33") |
| BinaryFeatures | Not yet run | osainz59/Ask2Transformers/a2t/tasks/base.py code served (permissive licence) · get_code("b7800bf465c4ead0") |
| BinaryTask | Not yet run | osainz59/Ask2Transformers/a2t/tasks/base.py code served (permissive licence) · get_code("e26beb437c1b9eca") |
| Features | Not yet run | osainz59/Ask2Transformers/a2t/tasks/base.py code served (permissive licence) · get_code("ac45f0bf994e8aaf") |
| IncorrectFeatureTypeError | Not yet run | osainz59/Ask2Transformers/a2t/tasks/base.py code served (permissive licence) · get_code("0d3c1b57b3dc81b1") |
| Task | Not yet run | osainz59/Ask2Transformers/a2t/tasks/base.py code served (permissive licence) · get_code("c9033c25890f4270") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples. In this demonstration we introduce a new workflow where the analyst directly verbalizes the entities/relations, which are then used by a Textual Entailment model to perform zero-shot IE. We present the design and implementation of a toolkit with a user interface, as well as experiments on four IE tasks that show that the system achieves very good performance at zero-shot learning using only 5-15 minutes per type of a user's effort. Our demonstration system is open-sourced at https:// github.com/BBN-E/ZS4IE. A demonstration video is available at https:// vimeo.com/676138340.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.13602")
get_code_for_paper("2203.13602")
have("2203.13602")
Connect an agent — have() is free.