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..
Mitigating Negative Style Transfer in Hybrid Dialogue System
Authors: Shimin Li, Qinyuan Cheng, Linyang Li, Xipeng Qiu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed extensive experiments on three dialogue datasets, including a hybrid dialogue dataset and two task-oriented dialogue datasets. The experimental results demonstrate that our method can mitigate the negative style transfer issue and achieves state-of-the-art performance on multiple dialogue datasets. |
| Researcher Affiliation | Academia | Shimin Li1, Qinyuan Cheng1, Linyang Li1, Xipeng Qiu1,2 *, 1 School of Computer Science, Fudan University 2 Shanghai Key Laboratory of Intelligent Information Processing, Fudan University EMAIL, EMAIL |
| Pseudocode | No | No clearly labeled pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | Code: https://github.com/whatissimondoing/Hi S-Dialog |
| Open Datasets | Yes | Fused Chat (Young et al. 2021) This dataset expands or rewrites each conversation based on the task-oriented task. Multi WOZ (Budzianowski et al. 2018; Eric et al. 2020) This dataset is one of the most prevalent datasets in task-oriented dialogue systems, collected via Wizard-of-Oz, and contains a total of 8438/1000/1000 multiturn dialogues. |
| Dataset Splits | Yes | Multi WOZ (Budzianowski et al. 2018; Eric et al. 2020) This dataset is one of the most prevalent datasets in task-oriented dialogue systems, collected via Wizard-of-Oz, and contains a total of 8438/1000/1000 multiturn dialogues. |
| Hardware Specification | Yes | All experiments were performed on a Ge Force RTX 3090 GPU (24G) |
| Software Dependencies | No | Hi S-Dialog and other baselines based on pre-trained models are implemented with Hugging Face s Transformers. No specific version numbers are provided for this or any other software. |
| Experiment Setup | Yes | We employ Adam W as the optimizer and configure the warmup rate to 0.1. For Fused Chat, the learning rate is 6e-4 and the batch size is set to 12 for 12 epochs. For Multi WOZ, we set the learning rate 5e-4, epoch 10, and batch size 12. |