Eduardo Blanco, Mosharaf Hossain, Dhivya Chinnappa
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 |
|---|---|---|
| mosharafhossain/negation-and-nlu | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| token_lens_to_idxs | Ran | mosharafhossain/negation-and-nlu/cue-detector/module/model.py pointer only (licence: NONE) · get_code("c6d63accbcc68eb7") |
| token_lens_to_offsets | Ran | mosharafhossain/negation-and-nlu/cue-detector/module/model.py pointer only (licence: NONE) · get_code("a14a46f0f9ee1d33") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper analyzes negation in eight popular corpora spanning six natural language understanding tasks. We show that these corpora have few negations compared to generalpurpose English, and that the few negations in them are often unimportant. Indeed, one can often ignore negations and still make the right predictions. Additionally, experimental results show that state-of-the-art transformers trained with these corpora obtain substantially worse results with instances that contain negation, especially if the negations are important. We conclude that new corpora accounting for negation are needed to solve natural language understanding tasks when negation is present.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.08929")
get_code_for_paper("2203.08929")
have("2203.08929")
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