31,449 research outputs found
Unsupervised Controllable Text Formalization
We propose a novel framework for controllable natural language
transformation. Realizing that the requirement of parallel corpus is
practically unsustainable for controllable generation tasks, an unsupervised
training scheme is introduced. The crux of the framework is a deep neural
encoder-decoder that is reinforced with text-transformation knowledge through
auxiliary modules (called scorers). The scorers, based on off-the-shelf
language processing tools, decide the learning scheme of the encoder-decoder
based on its actions. We apply this framework for the text-transformation task
of formalizing an input text by improving its readability grade; the degree of
required formalization can be controlled by the user at run-time. Experiments
on public datasets demonstrate the efficacy of our model towards: (a)
transforming a given text to a more formal style, and (b) introducing
appropriate amount of formalness in the output text pertaining to the input
control. Our code and datasets are released for academic use.Comment: AAA
A Universal Machine for Biform Theory Graphs
Broadly speaking, there are two kinds of semantics-aware assistant systems
for mathematics: proof assistants express the semantic in logic and emphasize
deduction, and computer algebra systems express the semantics in programming
languages and emphasize computation. Combining the complementary strengths of
both approaches while mending their complementary weaknesses has been an
important goal of the mechanized mathematics community for some time. We pick
up on the idea of biform theories and interpret it in the MMTt/OMDoc framework
which introduced the foundations-as-theories approach, and can thus represent
both logics and programming languages as theories. This yields a formal,
modular framework of biform theory graphs which mixes specifications and
implementations sharing the module system and typing information. We present
automated knowledge management work flows that interface to existing
specification/programming tools and enable an OpenMath Machine, that
operationalizes biform theories, evaluating expressions by exhaustively
applying the implementations of the respective operators. We evaluate the new
biform framework by adding implementations to the OpenMath standard content
dictionaries.Comment: Conferences on Intelligent Computer Mathematics, CICM 2013 The final
publication is available at http://link.springer.com
Syntactic Abstraction of B Models to Generate Tests
In a model-based testing approach as well as for the verification of
properties, B models provide an interesting solution. However, for industrial
applications, the size of their state space often makes them hard to handle. To
reduce the amount of states, an abstraction function can be used, often
combining state variable elimination and domain abstractions of the remaining
variables. This paper complements previous results, based on domain abstraction
for test generation, by adding a preliminary syntactic abstraction phase, based
on variable elimination. We define a syntactic transformation that suppresses
some variables from a B event model, in addition to a method that chooses
relevant variables according to a test purpose. We propose two methods to
compute an abstraction A of an initial model M. The first one computes A as a
simulation of M, and the second one computes A as a bisimulation of M. The
abstraction process produces a finite state system. We apply this abstraction
computation to a Model Based Testing process.Comment: Tests and Proofs 2010, Malaga : Spain (2010
CHR as grammar formalism. A first report
Grammars written as Constraint Handling Rules (CHR) can be executed as
efficient and robust bottom-up parsers that provide a straightforward,
non-backtracking treatment of ambiguity. Abduction with integrity constraints
as well as other dynamic hypothesis generation techniques fit naturally into
such grammars and are exemplified for anaphora resolution, coordination and
text interpretation.Comment: 12 pages. Presented at ERCIM Workshop on Constraints, Prague, Czech
Republic, June 18-20, 200
Towards Parameterized Regular Type Inference Using Set Constraints
We propose a method for inferring \emph{parameterized regular types} for
logic programs as solutions for systems of constraints over sets of finite
ground Herbrand terms (set constraint systems). Such parameterized regular
types generalize \emph{parametric} regular types by extending the scope of the
parameters in the type definitions so that such parameters can relate the types
of different predicates. We propose a number of enhancements to the procedure
for solving the constraint systems that improve the precision of the type
descriptions inferred. The resulting algorithm, together with a procedure to
establish a set constraint system from a logic program, yields a program
analysis that infers tighter safe approximations of the success types of the
program than previous comparable work, offering a new and useful efficiency vs.
precision trade-off. This is supported by experimental results, which show the
feasibility of our analysis
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