8 research outputs found
Successor-Invariant First-Order Logic on Classes of Bounded Degree
We study the expressive power of successor-invariant first-order logic, which
is an extension of first-order logic where the usage of an additional successor
relation on the structure is allowed, as long as the validity of formulas is
independent on the choice of a particular successor. We show that when the
degree is bounded, successor-invariant first-order logic is no more expressive
than first-order logic
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
The Eighteenth Data Release of the Sloan Digital Sky Surveys: Targeting and First Spectra from SDSS-V
The eighteenth data release of the Sloan Digital Sky Surveys (SDSS) is the
first one for SDSS-V, the fifth generation of the survey. SDSS-V comprises
three primary scientific programs, or "Mappers": Milky Way Mapper (MWM), Black
Hole Mapper (BHM), and Local Volume Mapper (LVM). This data release contains
extensive targeting information for the two multi-object spectroscopy programs
(MWM and BHM), including input catalogs and selection functions for their
numerous scientific objectives. We describe the production of the targeting
databases and their calibration- and scientifically-focused components. DR18
also includes ~25,000 new SDSS spectra and supplemental information for X-ray
sources identified by eROSITA in its eFEDS field. We present updates to some of
the SDSS software pipelines and preview changes anticipated for DR19. We also
describe three value-added catalogs (VACs) based on SDSS-IV data that have been
published since DR17, and one VAC based on the SDSS-V data in the eFEDS field.Comment: Accepted to ApJ
The eighteenth data release of the Sloan Digital Sky Surveys : targeting and first spectra from SDSS-V
The eighteenth data release of the Sloan Digital Sky Surveys (SDSS) is the first one for SDSS-V, the fifth generation of the survey. SDSS-V comprises three primary scientific programs, or "Mappers": Milky Way Mapper (MWM), Black Hole Mapper (BHM), and Local Volume Mapper (LVM). This data release contains extensive targeting information for the two multi-object spectroscopy programs (MWM and BHM), including input catalogs and selection functions for their numerous scientific objectives. We describe the production of the targeting databases and their calibration- and scientifically-focused components. DR18 also includes ~25,000 new SDSS spectra and supplemental information for X-ray sources identified by eROSITA in its eFEDS field. We present updates to some of the SDSS software pipelines and preview changes anticipated for DR19. We also describe three value-added catalogs (VACs) based on SDSS-IV data that have been published since DR17, and one VAC based on the SDSS-V data in the eFEDS field.Publisher PDFPeer reviewe