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..
MA-DST: Multi-Attention-Based Scalable Dialog State Tracking
Authors: Adarsh Kumar, Peter Ku, Anuj Goyal, Angeliki Metallinou, Dilek Hakkani-Tur8107-8114
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our model improves the joint goal accuracy by 5% (absolute) in the full-data setting and by up to 2% (absolute) in the zero-shot setting over the present state-of-the-art on the Multi Wo Z 2.1 dataset. We evaluate our approach on Multi WOZ, a multi-domain Wizard-of-Oz dataset. In this section we first describe the evaluation metrics and then present the results of our experiments. We compare the the accuracy of MA-DST with the TRADE baseline and four additional ablation variants of our model. |
| Researcher Affiliation | Collaboration | Adarsh Kumar,1 Peter Ku,2 Anuj Goyal,2 Angeliki Metallinou,2 Dilek Hakkani-Tur2 1University of Wisconsin-Madison, 2Amazon Alexa AI, Sunnyvale, CA, USA EMAIL, EMAIL |
| Pseudocode | No | The paper describes its model architecture and components but does not provide any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | Yes | We evaluate our approach on Multi WOZ, a multi-domain Wizard-of-Oz dataset. Multi WOZ 2.0 is a recent dataset of labeled human-human written conversations spanning multiple domains and topics (Budzianowski et al. 2018). ... (Eric et al. 2019) released an updated version, called Multi WOZ 2.1, which corrected a significant number of errors. Here, we use the Multi WOZ 2.1 dataset as our benchmark. |
| Dataset Splits | Yes | We use the provided train/dev/test split for our experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper mentions using GloVE, ELMo, GRU, and Adam Optimizer, but it does not specify any version numbers for these or other software libraries/frameworks. |
| Experiment Setup | Yes | We train the model using stochastic gradient descent and use the Adam Optimizer. We empirically optimized the learning rate in the range [0.0005 0.001] and used 0.0005 for the final model, while we kept betas as (0.9, 0.999) and epsilon 1x10 08. We used a batch size of four dialog turns and for each turn we generate all 30 slot values. We decayed the learning rate after regular intervals (3 epochs) by a factor of θ (0.25)... For ELMo, we kept a dropout of 0.5 for the contexual embedding and used l2 regularization for the weights of ELMo. We used a dropout of 0.2 for all the layers everywhere else. For word embeddings, we used 300-dimensional Glo Ve embeddings and 100-dimensional character embeddings. For all the GRU and attention layers the hidden size is kept at 400. The weight γ for the multi-task loss function in equation 18 is kept at 1. |