论文标题
潮湿：以任务为导向的对话双重对齐的多语言解析器
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
论文作者
论文摘要
现代虚拟助手使用内部语义解析引擎将用户话语转换为可操作的命令。但是，先前的工作表明，语义解析是一项艰巨的多语言转移任务，与其他任务相比，传递效率低。在印度和拉丁美洲等全球市场中，这是一个关键的问题，因为双语使用者之间的切换是普遍的。在这项工作中，我们使用两个多语言对齐的两个阶段的多语言和代码开关语义解析系统的零射击性能显着提高了零拍。首先，我们表明，预处理预处理可提高英语性能和转移效率。然后，我们引入了一种限制性的无参数对抗对齐方式的约束优化方法。我们的双重对齐的多语言解析器（潮湿）在Spanglish，hinglish，hinglish和多语言任务的基准测试基准上，Mbert转移性能提高了3倍，6倍和81倍，并使用3.2倍少于3.2倍的参数，分别超过了XLM-R-R和MT5-Large。
Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands. However, prior work has demonstrated that semantic parsing is a difficult multilingual transfer task with low transfer efficiency compared to other tasks. In global markets such as India and Latin America, this is a critical issue as switching between languages is prevalent for bilingual users. In this work we dramatically improve the zero-shot performance of a multilingual and codeswitched semantic parsing system using two stages of multilingual alignment. First, we show that constrastive alignment pretraining improves both English performance and transfer efficiency. We then introduce a constrained optimization approach for hyperparameter-free adversarial alignment during finetuning. Our Doubly Aligned Multilingual Parser (DAMP) improves mBERT transfer performance by 3x, 6x, and 81x on the Spanglish, Hinglish and Multilingual Task Oriented Parsing benchmarks respectively and outperforms XLM-R and mT5-Large using 3.2x fewer parameters.
