94,160 research outputs found
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
Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
Traditional semantic parsers map language onto compositional, executable
queries in a fixed schema. This mapping allows them to effectively leverage the
information contained in large, formal knowledge bases (KBs, e.g., Freebase) to
answer questions, but it is also fundamentally limiting---these semantic
parsers can only assign meaning to language that falls within the KB's
manually-produced schema. Recently proposed methods for open vocabulary
semantic parsing overcome this limitation by learning execution models for
arbitrary language, essentially using a text corpus as a kind of knowledge
base. However, all prior approaches to open vocabulary semantic parsing replace
a formal KB with textual information, making no use of the KB in their models.
We show how to combine the disparate representations used by these two
approaches, presenting for the first time a semantic parser that (1) produces
compositional, executable representations of language, (2) can successfully
leverage the information contained in both a formal KB and a large corpus, and
(3) is not limited to the schema of the underlying KB. We demonstrate
significantly improved performance over state-of-the-art baselines on an
open-domain natural language question answering task.Comment: Re-written abstract and intro, other minor changes throughout. This
version published at AAAI 201
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