Virtual assistants today require every website to submit skills individually into their proprietary repositories. The skill consists of a fixed set of supported commands and the formal representation of each command. The assistants use the contributed data to create a proprietary linguistic interface , typically using an intent classifier . This paper proposes  an open-source toolkit , called Schema2QA, that leverages the URL markup found in many websites to automatically build skills. Schema2QA has several advantages: (1) Schema2QA handles compositional queries involving multiple fields automatically, such as "find the Italian restaurant around here with the most reviews", or "what W3C employees on LinkedIn went to Oxford" ; (2)  Schema2QA translates natural language into executable queries on the up-to-date data from the website; (3) natural language training can be applied to one domain at a time to handle multiple websites using the same URL representations . 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 74\% and 78\%, and can answer questions that span three or more properties with 65\% accuracy. We also show that a new domain can be supported by transferring knowledge . The open-source Schema2QA lets each website create and own its linguistic interface.
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.
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today require every website to submit skills individually into their proprietary repositories. The skill consists of a fixed set of supported commands and the formal representation of each command. The assistants use the contributed data to create a proprietary linguistic interface , typically using an intent classifier
have proprietary third-party skill platforms; they train and own the voice interface to websites based on their submitted skill information
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Schema2QA,
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, called Schema2QA,
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handles
is more accurate than commercial assistants in answering
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fields automatically, such as "find the Italian restaurant around here with the most reviews", or "what W3C employees on LinkedIn went to Oxford"
properties
A
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.
R
translates natural language into executable queries on the up-to-date data from the website; (3) natural language training can be applied to one domain at a time to handle multiple websites using the same URL representations
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
R
74\% and 78\%, and can answer questions that span three or more properties with 65\% accuracy. We also
73\% and 81\%. With transfer learning, we
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be supported by transferring knowledge
achieve a 59\% accuracy without manual effort