Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code. Genie needs only a small realistic set of input sentences for validating the neural model. Developers write templates to synthesize data; Genie uses crowdsourced paraphrases and data augmentation, along with the synthesized data, to train a semantic parser. We also propose design principles that make VAPL languages amenable to natural language translation. We apply these principles to revise ThingTalk, the language used by the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's generality by showing a 19% and 31% improvement over the previous state of the art on a music skill, aggregate functions, and access control.Comment: To appear in PLDI 201

Contract Aware Components, 10 years after

The notion of contract aware components has been published roughly ten years ago and is now becoming mainstream in several fields where the usage of software components is seen as critical. The goal of this paper is to survey domains such as Embedded Systems or Service Oriented Architecture where the notion of contract aware components has been influential. For each of these domains we briefly describe what has been done with this idea and we discuss the remaining challenges.Comment: In Proceedings WCSI 2010, arXiv:1010.233

Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization

In this paper we study the personalized text search problem. The keyword based search method in conventional algorithms has a low efficiency in understanding users' intention since the semantic meaning, user profile, user interests are not always considered. Firstly, we propose a novel text search algorithm using a inverse filtering mechanism that is very efficient for label based item search. Secondly, we adopt the Bayesian network to implement the user interest prediction for an improved personalized search. According to user input, it searches the related items using keyword information, predicted user interest. Thirdly, the word vectorization is used to discover potential targets according to the semantic meaning. Experimental results show that the proposed search engine has an improved efficiency and accuracy and it can operate on embedded devices with very limited computational resources