44,901 research outputs found
Semantic memory
The Encyclopedia of Human Behavior, Second Edition is a comprehensive three-volume reference source on human action and reaction, and the thoughts, feelings, and physiological functions behind those actions
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
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering
User information needs vary significantly across different tasks, and
therefore their queries will also differ considerably in their expressiveness
and semantics. Many studies have been proposed to model such query diversity by
obtaining query types and building query-dependent ranking models. These
studies typically require either a labeled query dataset or clicks from
multiple users aggregated over the same document. These techniques, however,
are not applicable when manual query labeling is not viable, and aggregated
clicks are unavailable due to the private nature of the document collection,
e.g., in email search scenarios. In this paper, we study how to obtain query
type in an unsupervised fashion and how to incorporate this information into
query-dependent ranking models. We first develop a hierarchical clustering
algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine
query types. Then, we study three query-dependent ranking models, including two
neural models that leverage query type information as additional features, and
one novel multi-task neural model that views query type as the label for the
auxiliary query cluster prediction task. This multi-task model is trained to
simultaneously rank documents and predict query types. Our experiments on tens
of millions of real-world email search queries demonstrate that the proposed
multi-task model can significantly outperform the baseline neural ranking
models, which either do not incorporate query type information or just simply
feed query type as an additional feature.Comment: CIKM 201
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