Towards Diverse, Relevant and Coherent Open-Domain Dialogue Generation via Hybrid Latent Variables
Authors: Bin Sun, Yitong Li, Fei Mi, Weichao Wang, Yiwei Li, Kan Li
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two dialogue generation datasets (Daily Dialog and Opensubtitles) show that CHVT is superior to traditional transformer-based variational mechanism w.r.t. diversity, relevance and coherence metrics. |
| Researcher Affiliation | Collaboration | Bin Sun1, Yitong Li2,3, Fei Mi2, Weichao Wang2, Yiwei Li1, Kan Li1 1 School of Computer Science & Technology, Beijing Institute of Technology 2 Huawei Noah s Ark Lab 3 Huawei Technologies Ltd. {binsun,liyiwei,likan}@bit.edu.cn, {liyitong3,mifei2,wangweichao9}@huawei.com |
| Pseudocode | No | The paper describes methods and training steps but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'A full version is available at https://arxiv.org/abs/2212.01145.' but does not explicitly state that the source code for the methodology is provided, nor does it include a direct link to a code repository. |
| Open Datasets | Yes | We conduct extensive experiments on Daily Dialog (Li et al. 2017b) and Opensubtitles (Lison and Tiedemann 2016) datasets. |
| Dataset Splits | Yes | We collected all dialogue pairs, reduced the repeat pairs, and divided them into training, validation and test sets. Table 1 lists key statistics of our datasets. Daily Dialog # Train 68,066 # Valid 6,820 # Test 6,841. Open Subtitles # Train 200K # Valid 20K # Test 10K. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions general training techniques like 'KL annealing trick with a large warmup batch size' and 'lambda is the scale factor of KL divergence', but does not provide specific numerical values for hyperparameters such as learning rate, batch size, or epochs needed to reproduce the experiment setup. |