When learned without exploration, local models for structured prediction tasks are subject to exposure bias and cannot be trained without detailed guidance. Active Imitation Learning (AIL), also known in NLP as Dynamic Oracle Learning, is a general t
echnique for working around these issues by allowing the exploration of different outputs at training time. AIL requires oracle feedback: an oracle is any algorithm which can, given a partial candidate solution and gold annotation, find the correct (minimum loss) next output to produce. This paper describes a general finite state technique for deriving oracles. The technique describe is also efficient and will greatly expand the tasks for which AIL can be used.
Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Orde
red Memory (SOM). The model explicitly models the structure with an incremental parser and maintains the conditional probability setting of a standard language model (left-to-right). To train the incremental parser and avoid exposure bias, we also propose a novel dynamic oracle, so that SOM is more robust to wrong parsing decisions. Experiments show that SOM can achieve strong results in language modeling, incremental parsing, and syntactic generalization tests while using fewer parameters than other models.
In this research, We introduce two probabilistic mechanisms to
certificate parallel applications on distribute architecture supposing
that there are no oracles on which we depend on certification, in
addition to introducing cost model of two mecha
nisms and compare
them.
In this research, we are interested in parallel applications, which
are represented by data-flow graph that is built dynamically during
the execution and which are executed in a wide distributed
heterogeneous and dynamic environment and these applications
use the principle of work stealing to distribute the tasks among the
processors.
