# Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables

arXiv: https://arxiv.org/abs/1911.04081 · HTML: https://mlsanity.com/paper/1911.04081/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 21 of 21)

- [Language Models are Few-shot Multilingual Learners](https://mlsanity.com/paper/2109.07684/) (2021)
- [AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization](https://mlsanity.com/paper/2103.11332/) (2021)
- [From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2105.07316/) (2021)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension](https://mlsanity.com/paper/2104.12377/) (2021)
- [X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering](https://mlsanity.com/paper/2104.09696/) (2021)
- [AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All](https://mlsanity.com/paper/2112.08333/) (2021)
- [Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining](https://mlsanity.com/paper/2210.04782/) (2022)
- [CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System](https://mlsanity.com/paper/2106.03530/) (2021)
- [X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing](https://mlsanity.com/paper/2106.03777/) (2021)
- [Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding](https://mlsanity.com/paper/2109.01583/) (2021)
- [Transfer-Free Data-Efficient Multilingual Slot Labeling](https://mlsanity.com/paper/2305.13528/) (2023)
- [Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding](https://mlsanity.com/paper/2205.03656/) (2022)
- [ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language](https://mlsanity.com/paper/2408.08724/) (2024)
- [Cross-lingual Transfer for Automatic Question Generation by Learning Interrogative Structures in Target Languages](https://mlsanity.com/paper/2410.03197/) (2024)
- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)
- [Taming the Beast: Learning to Control Neural Conversational Models](https://mlsanity.com/paper/2108.10561/) (2021)
- [Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph](https://mlsanity.com/paper/2109.04400/) (2021)
- [Zero-shot Cross-lingual Conversational Semantic Role Labeling](https://mlsanity.com/paper/2204.04914/) (2022)
- [LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling](https://mlsanity.com/paper/2308.02903/) (2023)
- [Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch](https://mlsanity.com/paper/2410.18430/) (2024)

## Abstract (arXiv)

Despite the surging demands for multilingual task-oriented dialog systems (e.g., Alexa, Google Home), there has been less research done in multilingual or cross-lingual scenarios. Hence, we propose a zero-shot adaptation of task-oriented dialogue system to low-resource languages. To tackle this challenge, we first use a set of very few parallel word pairs to refine the aligned cross-lingual word-level representations. We then employ a latent variable model to cope with the variance of similar sentences across different languages, which is induced by imperfect cross-lingual alignments and inherent differences in languages. Finally, the experimental results show that even though we utilize much less external resources, our model achieves better adaptation performance for natural language understanding task (i.e., the intent detection and slot filling) compared to the current state-of-the-art model in the zero-shot scenario.