Before revision:  There is no need for a large annotated training set , which is expensive to acquire. The toolkit uses a pre-defined abstract dialogue state machine to synthesize millions of dialogues based on the domains' information. The annotated and synthesized data are used to train a contextual semantic parser that interprets the user's latest utterance in the context of a formal representation of the conversation up to that point.
After revision:  There is no need for a large annotated training set , which is used as the context in a semantic parser mapping the current user utterance to its formal meaning. In addition, we propose TOC (Task-Oriented Context), a formal dialogue state representation. This approach eliminates the need to parse a long history of natural language utterances; however, it adds complexity to the dialogue annotations. We propose Skim, a contextual semantic parser that interprets the user's latest utterance in the context of a formal representation of the conversation up to that point.