Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking
Nikita Moghe, Mark J. Steedman, Alexandra Birch · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 codemixed 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 Mul-tiWoZ (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.