ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfer
Authors: Zachary Horvitz, Ajay Patel, Chris Callison-Burch, Zhou Yu, Kathleen McKeown
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the method on the Enron Email Corpus, with both human and automatic evaluations, and find that it outperforms strong baselines on formality, sentiment, and even authorship style transfer. |
| Researcher Affiliation | Academia | 1 Columbia University 2 University of Pennsylvania |
| Pseudocode | Yes | Algorithm 1: Para Guide Style Transfer |
| Open Source Code | Yes | Our code is publicly available at https://github.com/ zacharyhorvitz/Para Guide. |
| Open Datasets | Yes | We evaluate our method on the Enron Email Corpus, which comprises several hundred thousand emails made public during the US government s investigation of Enron (Klimt and Yang 2004; Peterson, Hohensee, and Xia 2011). ... In addition to the Enron corpus, we also build a pretraining corpus from the Reddit Million User Dataset (MUD) (Andrews and Bishop 2019; Khan et al. 2021) |
| Dataset Splits | Yes | To build our training and validation datasets for attribute style transfer, we use popular existing formality and sentiment classifiers to score texts from the holdout authors in the Enron dataset. |
| Hardware Specification | No | The paper states that the method "can be fine-tuned on a single GPU" but does not specify the model or type of GPU, or any other hardware components. |
| Software Dependencies | No | The paper mentions software like SSD-LM RoBERTa-Large, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | PARAGUIDE s complete inference procedure is specified in Algorithm 1. ... We additionally visualize the affect of varying λ on authorship style transfer in Figure 2. When λ is small, the paraphrase-conditioned diffusion model reconstructs a more semantically faithful, fluent output. However, we can increase λ to improve Confusion scores, at the cost of semantic consistency and fluency. At the lowest setting, PARAGUIDE s Fluency and Similarity score are similar to those of Chat GPT-3.5 (0.78 vs 0.79 and 0.52 vs 0.56). |