用合成数据让大模型跨语言定位对话系统数据，更准更本地化。
CALICO: Conversational Agent Localization via Synthetic Data Generation
- 通过复制、翻译、本地化三步生成目标语言的实体值。
- 迭代过滤去噪后，下游对话系统性能显著提升。
- 适合做多语言对话系统的开发者和研究者参考。
我们提出CALICO，一种微调大型语言模型以实现对话代理训练数据跨语言定位的方法。针对槽位（命名实体），CALICO支持三种操作：原文复制、字面翻译和本地化，即生成更符合目标语言语境的槽位值，例如在该语言使用国的城市场所名称。此外，设计了一种迭代过滤机制以剔除噪声样本，实验证明可提升下游对话代理性能。为验证效果，构建并发布了多语言（8种）的全新人工本地化（HL）版MultiATIS++旅行信息测试集。相比原始人工翻译（HT）版本，新版本更具挑战性。实验表明，CALICO在HT场景下生成的槽位翻译更准确，在HL场景下生成的本地化槽位与真实HL测试集更接近，优于依赖上下文外字面翻译的前沿方法LINGUIST。
原文摘要 · Abstract (English)
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.
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