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A natural language interface to answers on the Web can help us access information more efficiently. We start with an interesting source of information-infoboxes in Wikipedia that summarize factoid knowledge-and develop a comprehensive approach to answering questions with high precision. We first build a system to access data in infoboxes in a structured manner. We use our system to construct a crowdsourced dataset of over 15,000 highquality, diverse questions. With these questions, we train a convolutional neural network model that outperforms models that achieve top results in similar answer selection tasks.

We present a new approach for building source-to-source transformations that can run on multiple programming languages, based on a new way of representing programs called incremental parametric syntax. We implement this approach in Haskell in our Cubix system, and construct incremental parametric syntaxes for C, Java, JavaScript, Lua, and Python. We demonstrate a whole-program refactoring tool that runs on all of them, along with three smaller transformations that each run on several. Our evaluation shows that (1) once a transformation is written, little work is required to configure it for a new language (2) transformations built this way output readable code which preserve the structure of the original, according to participants in our human study, and (3) our transformations can still handle language corner-cases, as validated on compiler test suites.

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