We lifted 3 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 |
|---|---|---|
| lifan-yuan/factmix | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| collate_fn | Ran | lifan-yuan/factmix/process_data.py code served (permissive licence) · get_code("8d44e37e16020525") |
| read_data | Ran | lifan-yuan/factmix/process_data.py code served (permissive licence) · get_code("4c0941ef29ca8ed3") |
| sampling | Ran | lifan-yuan/factmix/process_data.py code served (permissive licence) · get_code("d57e1fc9b24d799e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in cross-domain NER using a few labeled in-domain examples. This paper proposes a two-step rationale-centric data augmentation method to improve the model's generalization ability. Results on several datasets show that our model-agnostic method significantly improves the performance of cross-domain NER tasks compared to previous state-of-the-art methods, including the data augmentation and prompt-tuning methods. Our codes are available at https://github.com/lifan-yuan/FactMix.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2208.11464")
get_code_for_paper("2208.11464")
have("2208.11464")
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