Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Variational Memory Encoder-Decoder
Authors: Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,212 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,015 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically compare the proposed model against other recent approaches on various conversational datasets. The results show that VMED consistently achieves significant improvement over others in both metric-based and qualitative evaluations. |
| Researcher Affiliation | Academia | Hung Le, Truyen Tran, Thin Nguyen and Svetha Venkatesh Applied AI Institute, Deakin University, Geelong, Australia EMAIL |
| Pseudocode | Yes | Algorithm 1 VMED Generation |
| Open Source Code | Yes | Source code is available at https://github.com/thaihungle/VMED |
| Open Datasets | Yes | We perform experiments on two collections: The first collection includes open-domain movie transcript datasets containing casual conversations: Cornell Movies1 and Open Subtitle2. ... 1http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html 2http://opus.nlpl.eu/Open Subtitles.php |
| Dataset Splits | Yes | For each dataset, we use 10,000 conversations for validating and 10,000 for testing. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU/CPU models, memory, or specific computing platforms) used for conducting the experiments. |
| Software Dependencies | No | The paper mentions general software components and frameworks like RNNs, LSTMs, and Glove for word embeddings, but it does not specify exact version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper mentions trying the model with 'different number of modes (K = 1, 2, 3, 4)' which is a hyperparameter, but it states 'Details of dataset descriptions and model implementations are included in Supplementary material', implying other crucial experimental setup details like learning rates, batch sizes, or optimizer settings are not in the main text. |