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..
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
Authors: Shaoxiong Feng, Xuancheng Ren, Kan Li, Xu Sun12812-12820
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on two high-quality open-domain dialogue datasets, Daily Dialog and Persona Chat, compared with state-of-the-art methods, and provide extensive analysis to examine the effect of the proposed method. |
| Researcher Affiliation | Academia | Shaoxiong Feng,1 Xuancheng Ren,2 Kan Li,1 Xu Sun2,3 1School of Computer Science & Technology, Beijing Institute of Technology 2MOE Key Laboratory of Computational Linguistics, School of EECS, Peking University 3Center for Data Science, Peking University EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods using textual explanations and mathematical formulas, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing open-source code for the described methodology, nor does it provide links to any code repositories or mention supplementary materials containing code. |
| Open Datasets | Yes | We adopt two commonly-used dialogue datasets: Daily Dialog (Li et al. 2017b) and Persona Chat (Zhang et al. 2018a). |
| Dataset Splits | Yes | Finally, the processed dataset contains 50K, 4.5K, and 4.3K pairs for training, validation, and testing, respectively. (Daily Dialog) [...] The processed dataset contains 106K, 13K, and 12.5K pairs for training, validation, and testing. (Persona Chat) |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Adam optimizer (Kingma and Ba 2015)' but does not provide specific version numbers for any software dependencies, libraries, or frameworks used for the experiments. |
| Experiment Setup | Yes | We set the embedding size to 500, the vocabulary size for both Daily Dialog and Persona Chat to 20K. The dropout probability and the temperature T are 0.1 and 3, respectively. We use Adam optimizer (Kingma and Ba 2015), with a learning rate of 0.0001, gradient clipping at 5.0, and a mini-batch size of 64. [...] We set the number of students to 6 for DML and MRBD. The imitation probability in MRBD is 0.5. The training set is randomly divided into six non-overlapping subsets with the same number of pairs. |