Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests
Authors: Jiaao Chen, Diyi Yang12648-12656
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 {jchen896,dyang888}@gatech.edu |
| 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. |