Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents
Authors: Tom Young, Frank Xing, Vlad Pandelea, Jinjie Ni, Erik Cambria11622-11629
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate two baseline models on this task, including the classification-based two-stage models and the two-in-one fused models. We publicly release Fused Chat and the baselines to propel future work on inter-mode dialogue systems. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 School of Computing, National University of Singapore, Singapore |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | We publicly release Fused Chat and the baselines to propel future work on inter-mode dialogue systems. 1https://github.com/tomyoung903/Fused Chat |
| Open Datasets | Yes | Based on the popular TOD dataset Multi WOZ, we build a new dataset Fused Chat, by rewriting the existing TOD turns and adding new ODD turns. We publicly release Fused Chat and the baselines to propel future work on inter-mode dialogue systems. 1https://github.com/tomyoung903/Fused Chat |
| Dataset Splits | Yes | Table 2: Fused Chat is composed of ODD + TOD (prepending ODDs) instances and TOD + ODD (appending ODDs) instances. Partition Training Validation Testing Total Training 3670 4768 8438 Validation 500 500 1000 Testing 500 500 1000 Total 4670 5768 10438 |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions using pre-trained models like Dialo GPT, GPT2, and BERT, and follows approaches like Neural Pipeline, but it does not specify exact version numbers for these models or any other software dependencies like programming languages or libraries. |
| Experiment Setup | No | The paper mentions that models are 'fine-tuned' but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs), optimizer settings, or other training configurations. |