MOSS: End-to-End Dialog System Framework with Modular Supervision
Authors: Weixin Liang, Youzhi Tian, Chengcai Chen, Zhou Yu8327-8335
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Based on our experiments on both Laptop Network and Cam Rest676, we summarize the take-aways for how to efficiently build a dataset to solve a task. |
| Researcher Affiliation | Collaboration | 1Zhejiang University, 2University of California, Davis, 3Stanford University, 4Xiaoi Robot Technology Co., Ltd |
| Pseudocode | No | The paper describes the architecture and formulations using mathematical equations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release the code and data1. 1https://github.com/Youzhi Tian/MOSS-End-to-End-Dialog System-Framework-with-Modular-Supervision |
| Open Datasets | Yes | We release the code and data1. 1https://github.com/Youzhi Tian/MOSS-End-to-End-Dialog System-Framework-with-Modular-Supervision |
| Dataset Splits | Yes | We follow Wen et al. (2017b); Lei et al. (2018) to split the data as 3:1:1 for training, validation and testing. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming language versions or library versions. |
| Experiment Setup | No | The paper describes the model architecture and formulations but does not specify concrete experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |