We lifted 3 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| seilna/CNN-Units-in-NLP | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| sample_top | Ran | seilna/CNN-Units-in-NLP/code/utils.py code served (permissive licence) · get_code("889fdbb32959107f") |
| html_per_tas | Not yet run | seilna/CNN-Units-in-NLP/code/utils.py code served (permissive licence) · get_code("8198392778043324") |
| html_per_unit | Not yet run | seilna/CNN-Units-in-NLP/code/utils.py code served (permissive licence) · get_code("31e27ca4e46dca13") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in their intermediate layers. In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns. In order to quantitatively analyze such an intriguing phenomenon, we propose a concept alignment method based on how units respond to the replicated text. We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1902.07249")
get_code_for_paper("1902.07249")
have("1902.07249")
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