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..
End-to-End Transition-Based Online Dialogue Disentanglement
Authors: Hui Liu, Zhan Shi, Jia-Chen Gu, Quan Liu, Si Wei, Xiaodan Zhu
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on both the dataset we developed and the publicly available Ubuntu IRC dataset [Kummerfeld et al., 2019]. The results show that our model significantly outperforms the existing algorithms. |
| Researcher Affiliation | Collaboration | 1Ingenuity Labs Research Institute & ECE, Queen s University, Canada 2University of Science and Technology of China, Hefei, China 3State Key Laboratory of Cognitive Intelligence, i FLYTEK Research, Hefei, China |
| Pseudocode | No | The paper describes the model architecture and components in Section 4, but it does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | 1https://github.com/layneins/e2e-dialo-disentanglement |
| Open Datasets | Yes | To contribute to the research on disentanglement, we develop a large-scale dataset from online movie scripts. ... We publish our dataset to the research community. ... and the publicly available Ubuntu IRC dataset [Kummerfeld et al., 2019]. |
| Dataset Splits | Yes | We randomly split the dataset into 29,669/2036/2010 pairs for train/dev/test. ... We separate roughly every 50 continuous messages into a group and obtain 1737/134/104 pairs for train/dev/test, respectively. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions using "Glo Ve vectors [Pennington et al., 2014]" for word embedding and "Adam optimizer [Kingma and Ba, 2014]" but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We initialize word embedding using 300-dimension Glo Ve vectors [Pennington et al., 2014]. Other parameters are initialized by sampling from normal distribution with a standard deviation of 0.1. The mini-batch is 16 and size of hidden vectors in LSTM is 300. We use Adam optimizer [Kingma and Ba, 2014] with an initial learning rate of 5e-4. |