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Computing devices have recently become capable of interacting with their end users via natural language. However, they can only operate within a limited “supported” domain of discourse and fail drastically when faced with an out-of-domain utterance, mainly due to the limitations of their semantic parser. In this paper, we propose a semantic parser that generalizes to out-of-domain examples by learning a general strategy for parsing an unseen utterance through adapting the logical forms of seen utterances, instead of learning to generate a logical form from scratch. Our parser maintains a memory consisting of a representative subset of the seen utterances paired with their logical forms. Given an unseen utterance, our parser works by looking up a similar utterance from the memory and adapting its logical form until it fits the unseen utterance. Moreover, we present a data generation strategy for constructing utterance-logical form pairs from different domains. Our results show an improvement of up to 68.8% on one-shot parsing under two different evaluation settings compared to the baselines. 

A key challenge for generalizing programming-by-demonstration (PBD) scripts is the data description problem - when a user demonstrates performing an action, the system needs to determine features for describing this action and the target object in a way that can reflect the user's intention for the action. However, prior approaches for creating data descriptions in PBD systems have problems with usability, applicability, feasibility, transparency and/or user control. Our APPINITE system introduces a multimodal interface with which users can specify data descriptions verbally using natural language instructions. APPINITE guides users to describe their intentions for the demonstrated actions through mixed-initiative conversations. APPINITE constructs data descriptions for these actions from the natural language instructions. Our evaluation showed that APPINITE is easy-to-use and effective in creating scripts for tasks that would otherwise be difficult to create with prior PBD systems, due to ambiguous data descriptions in demonstrations on GUIs.