29,500 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
Abstract Meaning Representation for Multi-Document Summarization
Generating an abstract from a collection of documents is a desirable
capability for many real-world applications. However, abstractive approaches to
multi-document summarization have not been thoroughly investigated. This paper
studies the feasibility of using Abstract Meaning Representation (AMR), a
semantic representation of natural language grounded in linguistic theory, as a
form of content representation. Our approach condenses source documents to a
set of summary graphs following the AMR formalism. The summary graphs are then
transformed to a set of summary sentences in a surface realization step. The
framework is fully data-driven and flexible. Each component can be optimized
independently using small-scale, in-domain training data. We perform
experiments on benchmark summarization datasets and report promising results.
We also describe opportunities and challenges for advancing this line of
research.Comment: 13 page
Acquiring Word-Meaning Mappings for Natural Language Interfaces
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance
Data-Oriented Language Processing. An Overview
During the last few years, a new approach to language processing has started
to emerge, which has become known under various labels such as "data-oriented
parsing", "corpus-based interpretation", and "tree-bank grammar" (cf. van den
Berg et al. 1994; Bod 1992-96; Bod et al. 1996a/b; Bonnema 1996; Charniak
1996a/b; Goodman 1996; Kaplan 1996; Rajman 1995a/b; Scha 1990-92; Sekine &
Grishman 1995; Sima'an et al. 1994; Sima'an 1995-96; Tugwell 1995). This
approach, which we will call "data-oriented processing" or "DOP", embodies the
assumption that human language perception and production works with
representations of concrete past language experiences, rather than with
abstract linguistic rules. The models that instantiate this approach therefore
maintain large corpora of linguistic representations of previously occurring
utterances. When processing a new input utterance, analyses of this utterance
are constructed by combining fragments from the corpus; the
occurrence-frequencies of the fragments are used to estimate which analysis is
the most probable one.
In this paper we give an in-depth discussion of a data-oriented processing
model which employs a corpus of labelled phrase-structure trees. Then we review
some other models that instantiate the DOP approach. Many of these models also
employ labelled phrase-structure trees, but use different criteria for
extracting fragments from the corpus or employ different disambiguation
strategies (Bod 1996b; Charniak 1996a/b; Goodman 1996; Rajman 1995a/b; Sekine &
Grishman 1995; Sima'an 1995-96); other models use richer formalisms for their
corpus annotations (van den Berg et al. 1994; Bod et al., 1996a/b; Bonnema
1996; Kaplan 1996; Tugwell 1995).Comment: 34 pages, Postscrip
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