From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding
This work addresses the problem of limited evaluation data for low-resource languages in SLU, which is incremental by building on existing cross-lingual transfer methods.
The authors tackled the lack of training data for low-resource languages in Spoken Language Understanding by introducing a new benchmark (xSID) across 13 languages and proposing a joint learning approach using English SLU data with non-English auxiliary tasks, finding that masked language modeling improved slot filling and machine translation transfer enhanced intent classification.
The lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot filling require abundant training data, it is desirable to reuse existing data in high-resource languages to develop models for low-resource scenarios. We introduce xSID, a new benchmark for cross-lingual Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect. To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. We study two setups which differ by type and language coverage of the pre-trained embeddings. Our results show that jointly learning the main tasks with masked language modeling is effective for slots, while machine translation transfer works best for intent classification.