1,067 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
Learning a Neural Semantic Parser from User Feedback
We present an approach to rapidly and easily build natural language
interfaces to databases for new domains, whose performance improves over time
based on user feedback, and requires minimal intervention. To achieve this, we
adapt neural sequence models to map utterances directly to SQL with its full
expressivity, bypassing any intermediate meaning representations. These models
are immediately deployed online to solicit feedback from real users to flag
incorrect queries. Finally, the popularity of SQL facilitates gathering
annotations for incorrect predictions using the crowd, which is directly used
to improve our models. This complete feedback loop, without intermediate
representations or database specific engineering, opens up new ways of building
high quality semantic parsers. Experiments suggest that this approach can be
deployed quickly for any new target domain, as we show by learning a semantic
parser for an online academic database from scratch.Comment: Accepted at ACL 201
An End-to-end Neural Natural Language Interface for Databases
The ability to extract insights from new data sets is critical for decision
making. Visual interactive tools play an important role in data exploration
since they provide non-technical users with an effective way to visually
compose queries and comprehend the results. Natural language has recently
gained traction as an alternative query interface to databases with the
potential to enable non-expert users to formulate complex questions and
information needs efficiently and effectively. However, understanding natural
language questions and translating them accurately to SQL is a challenging
task, and thus Natural Language Interfaces for Databases (NLIDBs) have not yet
made their way into practical tools and commercial products.
In this paper, we present DBPal, a novel data exploration tool with a natural
language interface. DBPal leverages recent advances in deep models to make
query understanding more robust in the following ways: First, DBPal uses a deep
model to translate natural language statements to SQL, making the translation
process more robust to paraphrasing and other linguistic variations. Second, to
support the users in phrasing questions without knowing the database schema and
the query features, DBPal provides a learned auto-completion model that
suggests partial query extensions to users during query formulation and thus
helps to write complex queries
A reuse-Oriented Approach for the Construction of Scenario Bases Methods
International audienceDespite the recent interest in scenarios, the development of new methods and tools for Requirements Engineering integrating scenario based approaches has been limited. This paper reports on four different processes developed from research undertaken as part of the CREWS project which the authors believe will improve scenario use and make it more systematic. Furthermore CREWS aims to integrate these approaches into a method for scenario-based requirements engineering. To achieve this objective and be able to include existing approaches such as use case analysis we develop a component based approach which reflects a shift towards a reuse-centric approach to method engineering. The paper presents CREWS method and meta-method knowledge through the implementation of an SGML database to store, retrieve and dynamically compose chunks of CREWS processes
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