Jiaao Chen, Jon Saad-Falcon, Omar Shaikh, Duen Horng, Yang, Diyi Chau
We lifted 7 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| GT-SALT/Persuasive-Orderings | canonical | 4 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| check_ack_word | Ran | GT-SALT/Persuasive-Orderings/code/utils.py code served (permissive licence) · get_code("18e1fda964420c55") |
| get_samples | Ran | GT-SALT/Persuasive-Orderings/code/editing_utils.py code served (permissive licence) · get_code("c99352811ce651f3") |
| sentence_tokenize | Ran | GT-SALT/Persuasive-Orderings/code/utils.py code served (permissive licence) · get_code("cc5309b424f14414") |
| transform_format | Ran | GT-SALT/Persuasive-Orderings/code/utils.py code served (permissive licence) · get_code("92c8bed197d8b043") |
| fix_bad | Not yet run | GT-SALT/Persuasive-Orderings/code/editing_utils.py code served (permissive licence) · get_code("6ab05259e173403b") |
| get_content_strat_vector_details | Not yet run | GT-SALT/Persuasive-Orderings/code/dataset_iterators.py code served (permissive licence) · get_code("b0a783a0bb5478f4") |
| validate | Not yet run | GT-SALT/Persuasive-Orderings/code/vae_train/vae_utils.py code served (permissive licence) · get_code("e5ce471a81547224") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Interpreting how persuasive language influences audiences has implications across many domains like advertising, argumentation, and propaganda. Persuasion relies on more than a message's content. Arranging the order of the message itself (i.e., ordering specific rhetorical strategies) also plays an important role. To examine how strategy orderings contribute to persuasiveness, we first utilize a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus. We then visualize interplay between content and strategy through an attentional LSTM that predicts the success of textual requests. We find that specific (orderings of) strategies interact uniquely with a request's content to impact success rate, and thus the persuasiveness of a request.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.04625")
get_code_for_paper("2010.04625")
have("2010.04625")
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