Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

While communicating with a user, a task-oriented dialogue system has to track the user's
needs at each turn according to the conversation history. This process called dialogue state …

Since its launch in 2014, Amazon Echo family of devices has seen a considerable increase
in adaptation in consumer homes and offices. With a market worth millions of dollars, Echo is …

Most language understanding models in task-oriented dialog systems are trained on a small
amount of annotated training data, and evaluated in a small set from the same distribution …

Most prior work in dialogue modeling has been on written conversations mostly because of
existing data sets. However, written dialogues are not sufficient to fully capture the nature of …

Text augmentation is a strategy for increasing the diversity of training examples without
explicitly collecting new data. Owing to the efficiency and effectiveness of text augmentation …

As more users across the world are interacting with dialog agents in their daily life, there is a
need for better speech understanding that calls for renewed attention to the dynamics …

When a natural language generation (NLG) component is implemented in a real-world task-
oriented dialogue system, it is necessary to generate not only natural utterances as learned …

Language understanding in speech-based systems have attracted much attention in recent
years with the growing demand for voice interface applications. However, the robustness of …

Task-oriented dialogue systems have been plagued by the difficulties of obtaining large-
scale and high-quality annotated conversations. Furthermore, most of the publicly available …