@inproceedings{
rosenbaum2023calico,
title={{CALICO}: Conversational Agent Localization via Synthetic Data Generation},
author={Andy Rosenbaum and Pegah Kharazmi and Ershad Banijamali and Lu Zeng and Christopher DiPersio and Pan Wei and Gokmen Oz and Clement Chung and Karolina Owczarzak and Fabian Triefenbach and Wael Hamza},
booktitle={NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI},
year={2023},
url={https://openreview.net/forum?id=bpO14yuQQg}
}
We present CALICO, a method to fine-tune Large Language Models (LLMs) to localize conversational agent training data from one language to another. For slots (named entities), CALICO supports three operations: verbatim copy, literal translation, and localization, i.e. generating slot values more appropriate in the target language, such as city and airport names located in countries where the language is spoken. Furthermore, we design an iterative filtering mechanism to discard noisy generated samples, which we show boosts the performance of the downstream conversational agent. To prove the effectiveness of CALICO, we build and release a new human-localized (HL) version of the MultiATIS++ travel information test set in 8 languages. Compared to the original human-translated (HT) version of the test set, we show that our new HL version is more challenging. We also show that CALICO out-performs state-of-the-art LINGUIST (which relies on literal slot translation out of context) both on the HT case, where CALICO generates more accurate slot translations, and on the HL case, where CALICO generates localized slots which are closer to the HL test set.
CALICO: Conversational Agent Localization via Synthetic Data Generation
Andy Rosenbaum Pegah Kharazmi Ershad Banijamali Lu Zeng Christopher DiPersio Pan Wei Gokmen Oz Clement Chung Karolina Owczarzak Fabian Triefenbach Wael Hamza