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 | Conference PDF | Archive PDF | Plain Text | 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 {zliucr, giwinata, zlinao, pxuab}@connect.ust.hk, pascale@ece.ust.hk |
| 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. |