We lifted 2 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| simonroquette/CORAP | reimplementation | 1 of 1 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| colors | Ran | simonroquette/CORAP/predict.py pointer only (licence: NONE) · get_code("5a827e391309a388") |
| load_obj | Ran | this paper's copy was not recorded; identical code first harvested from zhuhai-ustc/limeattack pointer only · get_code("b91a5961fb87cb5e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Language processing mechanism by humans is generally more robust than computers. The Cmabrigde Uinervtisy (Cambridge University) effect from the psycholinguistics literature has demonstrated such a robust word processing mechanism, where jumbled words (e.g. Cmabrigde / Cambridge) are recognized with little cost. On the other hand, computational models for word recognition (e.g. spelling checkers) perform poorly on data with such noise. Inspired by the findings from the Cmabrigde Uinervtisy effect, we propose a word recognition model based on a semi-character level recurrent neural network (scRNN). In our experiments, we demonstrate that scRNN has significantly more robust performance in word spelling correction (i.e. word recognition) compared to existing spelling checkers and character-based convolutional neural network. Furthermore, we demonstrate that the model is cognitively plausible by replicating a psycholinguistics experiment about human reading difficulty using our model.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1608.02214")
get_code_for_paper("1608.02214")
have("1608.02214")
Connect an agent — have() is free.