Virtual assistants have proprietary third-party skill platforms; they train and own the voice interface to websites based on their submitted skill information . This paper proposes Schema2QA, an open-source toolkit that leverages the URL markup found in many websites to automatically build skills. Schema2QA has several advantages: (1) Schema2QA is more accurate than commercial assistants in answering compositional queries involving multiple properties; (2) it has a low-cost training data acquisition methodology that requires only writing a small number of annotations per domain and paraphrasing a small number of sentences. Schema2QA uses  a  novel neural model that combines the BERT pretrained model with an LSTM; the model can generalize a training set containing only synthesized and paraphrase data to understand real-world crowdsourced questions . We apply Schema2QA to two different domains, showing that the skills we built can answer useful queries with little manual effort . Our skills achieve an overall accuracy between 73\% and 81\% . With transfer learning , we show that a new domain can achieve a 59\% accuracy without manual effort. The open-source Schema2QA lets each website create and own its linguistic interface .
Building a question-answering virtual assistant skill currently requires large annotated datasets, which are too expensive to acquire for all but the largest companies . This paper proposes Schema2QA, an open-source toolkit that can build a Q A skill from a database schema, requiring just a few manual annotations on each field. The key concept is to cover the space of possible compound queries on the database with millions of domain-specific questions synthesized with the help of a corpus of generic query templates. The synthesized data and a small paraphrase set are used to train a novel neural network based on the BERT pretrained model . We apply Schema2QA to two different URL domains, restaurants and people, and show that the skills we built can answer complex queries accurately . Our skills achieve an overall accuracy between 74\% and 80\% on crowdsourced questions for the domains. Any websites with URL metadata in these domains can use Schema2QA to build their website-specific skill automatically. We show that with transfer learning from the restaurant to the hotel domain, we can achieve a 65\% accuracy on crowdsourced questions with no manual effort. On the restaurant domain, Schema2QA outperforms the best commercial assistant by 21\% on a set of diverse crowdsourced questions, while achieving equally good accuracy on questions commonly asked to existing virtual assistants .
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leverages the URL markup found in many websites to automatically build skills. Schema2QA has several advantages: (1) Schema2QA is more accurate than commercial assistants in answering compositional queries involving multiple properties; (2) it has a low-cost training data acquisition methodology that requires only writing a small number of annotations per domain and paraphrasing a small number of sentences. Schema2QA uses
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