We lifted 12 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| dadebias/genda-lens | pwc_unofficial | 10 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| get_coref_predictions | Ran | dadebias/genda-lens/genda_lens/coref_tasks/abc_utils.py code served (permissive licence) · get_code("00d3520c5093929f") |
| idx_pron | Ran | dadebias/genda-lens/genda_lens/lm_tasks/wino_utils.py code served (permissive licence) · get_code("5019b056504a4da4") |
| load_sents | Ran | dadebias/genda-lens/genda_lens/lm_tasks/abc_utils.py code served (permissive licence) · get_code("2ffc9b1a540aa3c7") |
| load_texts | Ran | dadebias/genda-lens/genda_lens/coref_tasks/wino_utils.py code served (permissive licence) · get_code("a3c6c64ab86bc773") |
| load_texts | Ran | dadebias/genda-lens/genda_lens/lm_tasks/wino_utils.py code served (permissive licence) · get_code("3f30ce652b7903ea") |
| logratio | Ran | dadebias/genda-lens/genda_lens/coref_tasks/wino_utils.py code served (permissive licence) · get_code("d63e61c158ee7c53") |
| remove_duplicates | Ran | dadebias/genda-lens/genda_lens/ner_tasks/process_names.py code served (permissive licence) · get_code("fe6f242ddb1d59bb") |
| remove_sq_br | Ran | dadebias/genda-lens/genda_lens/coref_tasks/wino_utils.py code served (permissive licence) · get_code("82a5a069748bdc70") |
| remove_sq_br | Ran | dadebias/genda-lens/genda_lens/lm_tasks/wino_utils.py code served (permissive licence) · get_code("4f9c8f00673b995a") |
| tokenize_sentence | Ran | dadebias/genda-lens/genda_lens/lm_tasks/abc_utils.py code served (permissive licence) · get_code("754ad37cfcc6370c") |
| load_mdl | Not yet run | dadebias/genda-lens/genda_lens/lm_tasks/load_model.py code served (permissive licence) · get_code("1b4325216f88a7d3") |
| load_mdl | Not yet run | dadebias/genda-lens/genda_lens/lm_tasks/abc_utils.py code served (permissive licence) · get_code("e77d690f3880683f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo's contextualized word vectors. First, we conduct several intrinsic analyses and find that (1) training data for ELMo contains significantly more male than female entities, (2) the trained ELMo embeddings systematically encode gender information and (3) ELMo unequally encodes gender information about male and female entities. Then, we show that a state-of-the-art coreference system that depends on ELMo inherits its bias and demonstrates significant bias on the WinoBias probing corpus. Finally, we explore two methods to mitigate such gender bias and show that the bias demonstrated on WinoBias can be eliminated.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1904.03310")
get_code_for_paper("1904.03310")
have("1904.03310")
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