941 research outputs found
LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task
This paper presents the LIG-CRIStAL submission to the shared Automatic Post-
Editing task of WMT 2017. We propose two neural post-editing models: a
monosource model with a task-specific attention mechanism, which performs
particularly well in a low-resource scenario; and a chained architecture which
makes use of the source sentence to provide extra context. This latter
architecture manages to slightly improve our results when more training data is
available. We present and discuss our results on two datasets (en-de and de-en)
that are made available for the task.Comment: keywords: neural post-edition, attention model
Duality symmetries and effective dynamics in disordered hopping models
We identify a duality transformation in one-dimensional hopping models that
relates propagators in general disordered potentials linked by an up-down
inversion of the energy landscape. This significantly generalises previous
results for a duality between trap and barrier models. We use the resulting
insights into the symmetries of these models to develop a real-space
renormalisation scheme that can be implemented computationally and allows
rather accurate prediction of propagation in these models. We also discuss the
relation of this renormalisation scheme to earlier analytical treatments.Comment: 29 pages, 7 figs. Final version, some extra context and references
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Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms
In NLP, convolutional neural networks (CNNs) have benefited less than
recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that
this is because the attention in CNNs has been mainly implemented as attentive
pooling (i.e., it is applied to pooling) rather than as attentive convolution
(i.e., it is integrated into convolution). Convolution is the differentiator of
CNNs in that it can powerfully model the higher-level representation of a word
by taking into account its local fixed-size context in the input text t^x. In
this work, we propose an attentive convolution network, ATTCONV. It extends the
context scope of the convolution operation, deriving higher-level features for
a word not only from local context, but also information extracted from
nonlocal context by the attention mechanism commonly used in RNNs. This
nonlocal context can come (i) from parts of the input text t^x that are distant
or (ii) from extra (i.e., external) contexts t^y. Experiments on sentence
modeling with zero-context (sentiment analysis), single-context (textual
entailment) and multiple-context (claim verification) demonstrate the
effectiveness of ATTCONV in sentence representation learning with the
incorporation of context. In particular, attentive convolution outperforms
attentive pooling and is a strong competitor to popular attentive RNNs.Comment: Camera-ready for TACL. 16 page
Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery
Deep learning tasks are often complicated and require a variety of components
working together efficiently to perform well. Due to the often large scale of
these tasks, there is a necessity to iterate quickly in order to attempt a
variety of methods and to find and fix bugs. While participating in IARPA's
Functional Map of the World challenge, we identified challenges along the
entire deep learning pipeline and found various solutions to these challenges.
In this paper, we present the performance, engineering, and deep learning
considerations with processing and modeling data, as well as underlying
infrastructure considerations that support large-scale deep learning tasks. We
also discuss insights and observations with regard to satellite imagery and
deep learning for image classification.Comment: Accepted to IEEE Big Data 201
The Guppy Effect as Interference
People use conjunctions and disjunctions of concepts in ways that violate the
rules of classical logic, such as the law of compositionality. Specifically,
they overextend conjunctions of concepts, a phenomenon referred to as the Guppy
Effect. We build on previous efforts to develop a quantum model that explains
the Guppy Effect in terms of interference. Using a well-studied data set with
16 exemplars that exhibit the Guppy Effect, we developed a 17-dimensional
complex Hilbert space H that models the data and demonstrates the relationship
between overextension and interference. We view the interference effect as, not
a logical fallacy on the conjunction, but a signal that out of the two
constituent concepts, a new concept has emerged.Comment: 10 page
Retrieval-Augmented Classification with Decoupled Representation
Retrieval augmented methods have shown promising results in various
classification tasks. However, existing methods focus on retrieving extra
context to enrich the input, which is noise sensitive and non-expandable. In
this paper, following this line, we propose a -nearest-neighbor (KNN) -based
method for retrieval augmented classifications, which interpolates the
predicted label distribution with retrieved instances' label distributions.
Different from the standard KNN process, we propose a decoupling mechanism as
we find that shared representation for classification and retrieval hurts
performance and leads to training instability. We evaluate our method on a wide
range of classification datasets. Experimental results demonstrate the
effectiveness and robustness of our proposed method. We also conduct extra
experiments to analyze the contributions of different components in our
model.\footnote{\url{https://github.com/xnliang98/knn-cls-w-decoupling}}Comment: preprin
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