论文标题
多任务学习用于低资源口语理解
Multitask Learning for Low Resource Spoken Language Understanding
论文作者
论文摘要
我们探讨了多任务学习为语音处理提供的好处，因为我们在具有自动语音识别和意图分类或情感分类的双重目标上训练模型。我们的模型虽然规模适中，但显示出比在意图分类的端到端训练的模型的改进。我们比较不同的设置，以找到每个任务模块的最佳处置。最后，我们通过训练每班一个示例的模型来研究模型在低资源场景中的性能。我们表明，在这些情况下，多任务学习与接受文本功能的基线模型竞争，并且比管道模型的表现要好得多。关于情感分类，我们匹配了端到端模型的性能，其参数是十倍。我们考虑使用荷兰语和英语的4个任务和4个数据集。
We explore the benefits that multitask learning offer to speech processing as we train models on dual objectives with automatic speech recognition and intent classification or sentiment classification. Our models, although being of modest size, show improvements over models trained end-to-end on intent classification. We compare different settings to find the optimal disposition of each task module compared to one another. Finally, we study the performance of the models in low-resource scenario by training the models with as few as one example per class. We show that multitask learning in these scenarios compete with a baseline model trained on text features and performs considerably better than a pipeline model. On sentiment classification, we match the performance of an end-to-end model with ten times as many parameters. We consider 4 tasks and 4 datasets in Dutch and English.
