Multi Domain Datasets

Multi-Domain learning (MDL) assumes that the domain labels in the dataset are known. However, when there are multiple metadata at- tributes available, it is not always straightfor- ward to select …...

AbstractPre-trained BERT contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data. While there has been a lot of focus on task-specific fine-tuning, there …...

AbstractTask-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains. Often, however, little to no target domain training data may …...

AbstractOn many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. Nevertheless, their huge model size and low inference speed …...

AbstractWe explore zero-shot adaptation, where a general-domain model has access to customer or domain specific parallel data at inference time, but not during training. We build on the idea of …...