151,941 research outputs found
Verifying UML/OCL operation contracts
In current model-driven development approaches, software models are the primary artifacts of the development process. Therefore, assessment of their correctness is a key issue to ensure the quality of the final application. Research on model consistency has focused mostly on the models' static aspects. Instead, this paper addresses the verification of their dynamic aspects, expressed as a set of operations defined by means of pre/postcondition contracts. This paper presents an automatic method based on Constraint Programming to verify UML models extended with OCL constraints and operation contracts. In our approach, both static and dynamic aspects are translated into a Constraint Satisfaction Problem. Then, compliance of the operations with respect to several correctness properties such as operation executability or determinism are formally verified
Target-Side Context for Discriminative Models in Statistical Machine Translation
Discriminative translation models utilizing source context have been shown to
help statistical machine translation performance. We propose a novel extension
of this work using target context information. Surprisingly, we show that this
model can be efficiently integrated directly in the decoding process. Our
approach scales to large training data sizes and results in consistent
improvements in translation quality on four language pairs. We also provide an
analysis comparing the strengths of the baseline source-context model with our
extended source-context and target-context model and we show that our extension
allows us to better capture morphological coherence. Our work is freely
available as part of Moses.Comment: Accepted as a long paper for ACL 201
A Shared Task on Bandit Learning for Machine Translation
We introduce and describe the results of a novel shared task on bandit
learning for machine translation. The task was organized jointly by Amazon and
Heidelberg University for the first time at the Second Conference on Machine
Translation (WMT 2017). The goal of the task is to encourage research on
learning machine translation from weak user feedback instead of human
references or post-edits. On each of a sequence of rounds, a machine
translation system is required to propose a translation for an input, and
receives a real-valued estimate of the quality of the proposed translation for
learning. This paper describes the shared task's learning and evaluation setup,
using services hosted on Amazon Web Services (AWS), the data and evaluation
metrics, and the results of various machine translation architectures and
learning protocols.Comment: Conference on Machine Translation (WMT) 201
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