# An Empirical Study of Cross-Lingual Transferability in Generative Dialogue State Tracker

arXiv: https://arxiv.org/abs/2101.11360 · HTML: https://mlsanity.com/paper/2101.11360/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://mlsanity.com/paper/1606.03777/) (2016)
- [Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog](https://mlsanity.com/paper/1810.13327/) (2018)
- [Efficient Dialogue State Tracking by Selectively Overwriting Memory](https://mlsanity.com/paper/1911.03906/) (2019)
- [Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems](https://mlsanity.com/paper/1911.09273/) (2019)
- [Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing](https://mlsanity.com/paper/1807.06517/) (2018)
- [Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints](https://mlsanity.com/paper/1706.00374/) (2017)
- [A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking](https://mlsanity.com/paper/2006.01554/) (2020)

## Built on by (most cited first, 2 of 2)

- [Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking](https://mlsanity.com/paper/2109.13620/) (2021)
- [Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch](https://mlsanity.com/paper/2410.18430/) (2024)

## Abstract (arXiv)

There has been a rapid development in data-driven task-oriented dialogue systems with the benefit of large-scale datasets. However, the progress of dialogue systems in low-resource languages lags far behind due to the lack of high-quality data. To advance the cross-lingual technology in building dialog systems, DSTC9 introduces the task of cross-lingual dialog state tracking, where we test the DST module in a low-resource language given the rich-resource training dataset. This paper studies the transferability of a cross-lingual generative dialogue state tracking system using a multilingual pre-trained seq2seq model. We experiment under different settings, including joint-training or pre-training on cross-lingual and cross-ontology datasets. We also find out the low cross-lingual transferability of our approaches and provides investigation and discussion.