论文标题
语言学家：语言模型指令调整以生成带注释的话语，以进行意图分类和插槽标记
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging
论文作者
论文摘要
我们介绍了语言学家，这是一种通过微调Alexatm 5B生成带注释的数据进行意图分类和插槽标记（IC+ST）的方法。在SNIP数据集的10次新颖意图设置中，语言学家超过了最新的方法（反向翻译和示例外推），可以宽阔的边缘，显示出IC回忆的目标意图的绝对改善，而ST F1得分的目标率为+2.5分。在MATIS ++数据集的零射击跨语言设置中，语言学家表现出强大的机器翻译基线，插槽对齐的插槽对齐+4.14 +4.14在6个语言中绝对在ST F1分数上绝对，同时匹配IC上的性能。最后，我们在用于对话代理IC+ST的内部大型多语言数据集上验证了我们的结果，并显示了使用背面翻译，释义和插槽目录重新采样采样的基线的显着改进。据我们所知，我们是第一个展示大规模SEQ2SEQ模型的教学微调的人，以控制多语言意图和插槽标记的数据生成的输出。
We present LINGUIST, a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual sequence-to-sequence (seq2seq) model, on a flexible instruction prompt. In a 10-shot novel intent setting for the SNIPS dataset, LINGUIST surpasses state-of-the-art approaches (Back-Translation and Example Extrapolation) by a wide margin, showing absolute improvement for the target intents of +1.9 points on IC Recall and +2.5 points on ST F1 Score. In the zero-shot cross-lingual setting of the mATIS++ dataset, LINGUIST out-performs a strong baseline of Machine Translation with Slot Alignment by +4.14 points absolute on ST F1 Score across 6 languages, while matching performance on IC. Finally, we verify our results on an internal large-scale multilingual dataset for conversational agent IC+ST and show significant improvements over a baseline which uses Back-Translation, Paraphrasing and Slot Catalog Resampling. To our knowledge, we are the first to demonstrate instruction fine-tuning of a large-scale seq2seq model to control the outputs of multilingual intent- and slot-labeled data generation.
