Abstract
Recent progress in task-oriented neural dialogue systems is largely focused on a handful of languages, as annotation of training data is tedious and expensive. Machine translation has been used to make systems multilingual, but this can introduce a pipeline of errors. Another promising solution is using cross-lingual transfer learning through pretrained multilingual models. Existing methods train multilingual models with additional code-mixed task data or refine the cross-lingual representations through parallel ontologies. In this work, we enhance the transfer learning process by intermediate fine-tuning of pretrained multilingual models, where the multilingual models are fine-tuned with different but related data and/or tasks. Specifically, we use parallel and conversational movie subtitles datasets to design cross-lingual intermediate tasks suitable for downstream dialogue tasks. We use only 200K lines of parallel data for intermediate fine-tuning which is already available for 1782 language pairs. We test our approach on the cross-lingual dialogue state tracking task for the parallel MultiWoZ (English -> Chinese, Chinese -> English) and Multilingual WoZ (English -> German, English -> Italian) datasets. We achieve impressive improvements (> 20% on joint goal accuracy) on the parallel MultiWoZ dataset and the Multilingual WoZ dataset over the vanilla baseline with only 10% of the target language task data and zero-shot setup respectively.- Anthology ID:
- 2021.emnlp-main.87
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1137–1150
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.87
- DOI:
- 10.18653/v1/2021.emnlp-main.87
- Cite (ACL):
- Nikita Moghe, Mark Steedman, and Alexandra Birch. 2021. Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1137–1150, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking (Moghe et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.emnlp-main.87.pdf
- Code
- nikitacs16/xlift_dst
- Data
- OpenSubtitles