6,418 research outputs found
Document Meta-Information as Weak Supervision for Machine Translation
Data-driven machine translation has advanced considerably since the first pioneering work
in the 1990s with recent systems claiming human parity on sentence translation for highresource tasks. However, performance degrades for low-resource domains with no available
sentence-parallel training data. Machine translation systems also rarely incorporate the
document context beyond the sentence level, ignoring knowledge which is essential for
some situations. In this thesis, we aim to address the two issues mentioned above by
examining ways to incorporate document-level meta-information into data-driven machine
translation. Examples of document meta-information include document authorship and
categorization information, as well as cross-lingual correspondences between documents,
such as hyperlinks or citations between documents. As this meta-information is much more
coarse-grained than reference translations, it constitutes a source of weak supervision for
machine translation. We present four cumulatively conducted case studies where we devise
and evaluate methods to exploit these sources of weak supervision both in low-resource
scenarios where no task-appropriate supervision from parallel data exists, and in a full
supervision scenario where weak supervision from document meta-information is used to
supplement supervision from sentence-level reference translations. All case studies show
improved translation quality when incorporating document meta-information
Accelerating Innovation Through Analogy Mining
The availability of large idea repositories (e.g., the U.S. patent database)
could significantly accelerate innovation and discovery by providing people
with inspiration from solutions to analogous problems. However, finding useful
analogies in these large, messy, real-world repositories remains a persistent
challenge for either human or automated methods. Previous approaches include
costly hand-created databases that have high relational structure (e.g.,
predicate calculus representations) but are very sparse. Simpler
machine-learning/information-retrieval similarity metrics can scale to large,
natural-language datasets, but struggle to account for structural similarity,
which is central to analogy. In this paper we explore the viability and value
of learning simpler structural representations, specifically, "problem
schemas", which specify the purpose of a product and the mechanisms by which it
achieves that purpose. Our approach combines crowdsourcing and recurrent neural
networks to extract purpose and mechanism vector representations from product
descriptions. We demonstrate that these learned vectors allow us to find
analogies with higher precision and recall than traditional
information-retrieval methods. In an ideation experiment, analogies retrieved
by our models significantly increased people's likelihood of generating
creative ideas compared to analogies retrieved by traditional methods. Our
results suggest a promising approach to enabling computational analogy at scale
is to learn and leverage weaker structural representations.Comment: KDD 201
Learning Human Pose Estimation Features with Convolutional Networks
This paper introduces a new architecture for human pose estimation using a
multi- layer convolutional network architecture and a modified learning
technique that learns low-level features and higher-level weak spatial models.
Unconstrained human pose estimation is one of the hardest problems in computer
vision, and our new architecture and learning schema shows significant
improvement over the current state-of-the-art results. The main contribution of
this paper is showing, for the first time, that a specific variation of deep
learning is able to outperform all existing traditional architectures on this
task. The paper also discusses several lessons learned while researching
alternatives, most notably, that it is possible to learn strong low-level
feature detectors on features that might even just cover a few pixels in the
image. Higher-level spatial models improve somewhat the overall result, but to
a much lesser extent then expected. Many researchers previously argued that the
kinematic structure and top-down information is crucial for this domain, but
with our purely bottom up, and weak spatial model, we could improve other more
complicated architectures that currently produce the best results. This mirrors
what many other researchers, like those in the speech recognition, object
recognition, and other domains have experienced
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 130, July 1974
This special bibliography lists 291 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1974
Entry and Patenting in the Software Industry
To what extent are firms kept out of a market by patents covering related technologies? Do patents held by potential entrants make it easier to enter markets? We estimate the empirical relationship between market entry and patents for 27 narrowly defined categories of software products during the period 1990-2004. Controlling for demand, market structure, average patent quality, and other factors, we find that a 10% increase in the number of patents relevant to market reduces the rate of entry by 3-8%, and this relationship intensified following expansions in the patentability of software in the mid-1990s. However, potential entrants with patent applications relevant to a market are more likely to enter it. Finally, patents appear to substitute for complementary assets in the entry process, as patents have both greater entry-deterring and entry-promoting effects for firms without prior experience in other markets.
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