286 research outputs found
Neural Machine Translation for Code Generation
Neural machine translation (NMT) methods developed for natural language
processing have been shown to be highly successful in automating translation
from one natural language to another. Recently, these NMT methods have been
adapted to the generation of program code. In NMT for code generation, the task
is to generate output source code that satisfies constraints expressed in the
input. In the literature, a variety of different input scenarios have been
explored, including generating code based on natural language description,
lower-level representations such as binary or assembly (neural decompilation),
partial representations of source code (code completion and repair), and source
code in another language (code translation). In this paper we survey the NMT
for code generation literature, cataloging the variety of methods that have
been explored according to input and output representations, model
architectures, optimization techniques used, data sets, and evaluation methods.
We discuss the limitations of existing methods and future research directionsComment: 33 pages, 1 figur
Bayesian Inference of Recursive Sequences of Group Activities from Tracks
We present a probabilistic generative model for inferring a description of
coordinated, recursively structured group activities at multiple levels of
temporal granularity based on observations of individuals' trajectories. The
model accommodates: (1) hierarchically structured groups, (2) activities that
are temporally and compositionally recursive, (3) component roles assigning
different subactivity dynamics to subgroups of participants, and (4) a
nonparametric Gaussian Process model of trajectories. We present an MCMC
sampling framework for performing joint inference over recursive activity
descriptions and assignment of trajectories to groups, integrating out
continuous parameters. We demonstrate the model's expressive power in several
simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event
video data sets.Comment: 10 pages, 6 figures, in Proceedings of the 30th AAAI Conference on
Artificial Intelligence (AAAI'16), Phoenix, AZ, 201
Learning what to read: Focused machine reading
Recent efforts in bioinformatics have achieved tremendous progress in the
machine reading of biomedical literature, and the assembly of the extracted
biochemical interactions into large-scale models such as protein signaling
pathways. However, batch machine reading of literature at today's scale (PubMed
alone indexes over 1 million papers per year) is unfeasible due to both cost
and processing overhead. In this work, we introduce a focused reading approach
to guide the machine reading of biomedical literature towards what literature
should be read to answer a biomedical query as efficiently as possible. We
introduce a family of algorithms for focused reading, including an intuitive,
strong baseline, and a second approach which uses a reinforcement learning (RL)
framework that learns when to explore (widen the search) or exploit (narrow
it). We demonstrate that the RL approach is capable of answering more queries
than the baseline, while being more efficient, i.e., reading fewer documents.Comment: 6 pages, 1 figure, 1 algorithm, 2 tables, accepted to EMNLP 201
Sustainability appraisal: Jack of all trades, master of none?
Sustainable development is a commonly quoted goal for decision making and supports a large number of other discourses. Sustainability appraisal has a stated goal of supporting decision making for sustainable development. We suggest that the inherent flexibility of sustainability appraisal facilitates outcomes that often do not adhere to the three goals enshrined in most definitions of sustainable development: economic growth, environmental protection and enhancement, and the wellbeing of the human population. Current practice is for sustainable development to be disenfranchised through the interpretation of sustainability, whereby the best alternative is good enough even when unsustainable. Practitioners must carefully and transparently review the frameworks applied during sustainability appraisal to ensure that outcomes will meet the three goals, rather than focusing on a discourse that emphasises one or more goals at the expense of the other(s)
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