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..
Attention-Informed Mixed-Language Training for Zero-Shot Cross-Lingual Task-Oriented Dialogue Systems
Authors: Zihan Liu, Genta Indra Winata, Zhaojiang Lin, Peng Xu, Pascale Fung8433-8440
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | intensive experiments with different cross-lingual embeddings demonstrate the effectiveness of our approach. |
| Researcher Affiliation | Academia | Zihan Liu, Genta Indra Winata, Zhaojiang Lin, Peng Xu, Pascale Fung Center for Artificial Intelligence Research (CAi RE) The Hong Kong University of Science and Technology EMAIL, EMAIL |
| Pseudocode | No | The paper describes the proposed method using text and diagrams (Figure 1, Figure 2, Figure 3), but it does not include any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | The code is available at: https://github.com/zliucr/mixedlanguage-training |
| Open Datasets | Yes | Wizard of Oz (WOZ), a restaurant domain dataset, is used for training and evaluating dialogue state tracking models on English. It was enlarged into WOZ 2.0 by adding more dialogues, and recently, Mrkˇsi c et al. (2017b) expanded WOZ 2.0 into Multilingual WOZ 2.0 by including two more languages (German and Italian). ... Recently, a multilingual task-oriented natural language understanding dialogue dataset was proposed by Schuster et al. (2019), which contains English, Spanish, and Thai across three domains (alarm, reminder, and weather). |
| Dataset Splits | Yes | Multilingual WOZ 2.0 contains 1200 dialogues for each language, where 600 dialogues are used for training, 200 for validation, and 400 for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper discusses training settings and baselines but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed system-level training configurations. |