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..
MALA: Cross-Domain Dialogue Generation with Action Learning
Authors: Xinting Huang, Jianzhong Qi, Yu Sun, Rui Zhang7977-7984
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments using multi-domain datasets, SMD and Multi WOZ, show that our proposed model achieves consistent improvements over the baselines models in terms of both task completion and language quality. |
| Researcher Affiliation | Collaboration | 1The University of Melbourne, 2Twitter Inc. |
| Pseudocode | No | The paper describes its method using textual descriptions and mathematical formulations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We use two multi-domain human-human conversational datasets: (1) SMD dataset (Eric and Manning 2017) contains 2425 dialogues, and has three domains: calendar, weather, navigation; (2) MULTIWOZ dataset (Budzianowski et al. 2018) is the largest existing taskoriented corpus spanning over seven domains. |
| Dataset Splits | Yes | We use the separation of training, validation and testing data as original SMD and MULTIWOZ dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'three-layer transformer' and 'VQ-VAE' as components but does not provide specific version numbers for any software, libraries, or frameworks used in the implementation or experimentation. |
| Experiment Setup | No | The paper mentions some architectural details for the base model, such as 'a three-layer transformer... with a hidden size of 128 and 4 heads', but does not provide specific hyperparameters like learning rate, batch size, or number of epochs, nor a detailed training configuration. |