Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests
Authors: Jiaao Chen, Diyi Yang12648-12656
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results showed that our proposed method outperformed existing semi-supervised baselines significantly. |
| Researcher Affiliation | Academia | Jiaao Chen, Diyi Yang School of Interactive Computing Georgia Institute of Technology EMAIL |
| Pseudocode | No | The paper describes the model architecture and training process in text, but no formal pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | We have publicly released our code at https://github.com/GT-SALT/Persuasion Strategy WVAE. |
| Open Datasets | No | The paper describes the creation of a new multi-domain text corpus but does not provide concrete access information (e.g., specific link, DOI, repository name, or formal citation) for the dataset itself. |
| Dataset Splits | Yes | Table 3: Split statistics about train, dev, and test set. Dataset Train Dev Test Borrow 900 400 400 RAOP 300 200 300 Kiva 1000 400 400 |
| Hardware Specification | No | We acknowledge the support of NVIDIA Corporation with the donation of GPU used for this research. |
| Software Dependencies | No | The paper mentions software components like NLTK, BERT, LSTM, MLP, and Adam W, but does not specify their version numbers or other specific software dependencies required for replication. |
| Experiment Setup | No | The paper states, 'tuned hyper-parameters on the development set' and refers to an appendix for 'Parameters details', but these details are not provided within the main text of the paper given. |