3,037 research outputs found
Handling Homographs in Neural Machine Translation
Homographs, words with different meanings but the same surface form, have
long caused difficulty for machine translation systems, as it is difficult to
select the correct translation based on the context. However, with the advent
of neural machine translation (NMT) systems, which can theoretically take into
account global sentential context, one may hypothesize that this problem has
been alleviated. In this paper, we first provide empirical evidence that
existing NMT systems in fact still have significant problems in properly
translating ambiguous words. We then proceed to describe methods, inspired by
the word sense disambiguation literature, that model the context of the input
word with context-aware word embeddings that help to differentiate the word
sense be- fore feeding it into the encoder. Experiments on three language pairs
demonstrate that such models improve the performance of NMT systems both in
terms of BLEU score and in the accuracy of translating homographs.Comment: NAACL201
Learning Character-level Compositionality with Visual Features
Previous work has modeled the compositionality of words by creating
character-level models of meaning, reducing problems of sparsity for rare
words. However, in many writing systems compositionality has an effect even on
the character-level: the meaning of a character is derived by the sum of its
parts. In this paper, we model this effect by creating embeddings for
characters based on their visual characteristics, creating an image for the
character and running it through a convolutional neural network to produce a
visual character embedding. Experiments on a text classification task
demonstrate that such model allows for better processing of instances with rare
characters in languages such as Chinese, Japanese, and Korean. Additionally,
qualitative analyses demonstrate that our proposed model learns to focus on the
parts of characters that carry semantic content, resulting in embeddings that
are coherent in visual space.Comment: Accepted to ACL 201
The [Ne III] Jet of DG Tau and its Ionization Scenarios
Forbidden neon emission from jets of low-mass young stars can be used to
probe the underlying high-energy processes in these systems. We analyze spectra
of the jet of DG Tau obtained with the Very Large Telescope/X-Shooter
spectrograph in 2010. [Ne III] 3869 is clearly detected in the
innermost 3" microjet and the outer knot located at 6".5. The velocity
structure of the inner microjet can be decomposed into the low-velocity
component (LVC) at km/s and the high-velocity component (HVC) at
km/s. Based on the observed [Ne III] flux and its spatial extent,
we suggest the origins of the [Ne III] emission regions and their relation with
known X-ray sources along the jet. The flares from the hard X-ray source close
to the star may be the main ionization source of the innermost microjet. The
fainter soft X-ray source at 0".2 from the star may provide sufficient heating
to help to sustain the ionization fraction against the recombination in the
flow. The outer knot may be reionized by shocks faster than 100 km/s such that
[Ne III] emission reappears and that the soft X-ray emission at 5".5 is
produced. Velocity decomposition of the archival Hubble Space Telescope spectra
obtained in 1999 shows that the HVC had been faster, with a velocity centroid
of km/s. Such a decrease in velocity may potentially be explained
by the expansion of the stellar magnetosphere, changing the truncation radius
and thus the launching speed of the jet. The energy released by magnetic
reconnections during relaxation of the transition can heat the gas up to
several tens of megakelvin and provide the explanation for on-source keV X-ray
flares that ionize the neon microjet
Velocity-Resolved [Ne III] from X-Ray Irradiated Sz 102 Microjets
Neon emission lines are good indicators of high-excitation regions close to a
young stellar system because of their high ionization potentials and large
critical densities. We have discovered [Ne III]{\lambda}3869 emission from the
microjets of Sz 102, a low-mass young star in Lupus III. Spectroastrometric
analyses of two-dimensional [Ne III] spectra obtained from archival
high-dispersion () Very Large Telescope/UVES data suggest that
the emission consists of two velocity components spatially separated by ~ 0."3,
or a projected distance of ~ 60 AU. The stronger redshifted component is
centered at ~ +21 km/s with a line width of ~ 140 km/s, and the weaker
blueshifted component at ~ -90 km/s with a line width of ~ 190 km/s. The two
components trace velocity centroids of the known microjets and show large line
widths that extend across the systemic velocity, suggesting their potential
origins in wide-angle winds that may eventually collimate into jets. Optical
line ratios indicate that the microjets are hot ( K)
and ionized ( cm). The blueshifted component
has ~ 13% higher temperature and ~ 46% higher electron density than the
redshifted counterpart, forming a system of asymmetric pair of jets. The
detection of the [Ne III]{\lambda}3869 line with the distinct velocity profile
suggests that the emission originates in flows that may have been strongly
ionized by deeply embedded hard X-ray sources, most likely generated by
magnetic processes. The discovery of [Ne III]{\lambda}3869 emission along with
other optical forbidden lines from Sz 102 support the picture of wide-angle
winds surrounding magnetic loops in the close vicinity of the young star.
Future high sensitivity X-ray imaging and high angular-resolution optical
spectroscopy may help confirm the picture proposed.Comment: 33 pages, 9 figures, 2 tables; accepted for publication in the ApJ
(minor typo and reference list fixed
Bayesian estimation and reconstruction of marine surface contaminant dispersion
Discharge of hazardous substances into the marine environment poses a
substantial risk to both public health and the ecosystem. In such incidents, it
is imperative to accurately estimate the release strength of the source and
reconstruct the spatio-temporal dispersion of the substances based on the
collected measurements. In this study, we propose an integrated estimation
framework to tackle this challenge, which can be used in conjunction with a
sensor network or a mobile sensor for environment monitoring. We employ the
fundamental convection-diffusion partial differential equation (PDE) to
represent the general dispersion of a physical quantity in a non-uniform flow
field. The PDE model is spatially discretised into a linear state-space model
using the dynamic transient finite-element method (FEM) so that the
characterisation of time-varying dispersion can be cast into the problem of
inferring the model states from sensor measurements. We also consider imperfect
sensing phenomena, including miss-detection and signal quantisation, which are
frequently encountered when using a sensor network. This complicated sensor
process introduces nonlinearity into the Bayesian estimation process. A
Rao-Blackwellised particle filter (RBPF) is designed to provide an effective
solution by exploiting the linear structure of the state-space model, whereas
the nonlinearity of the measurement model can be handled by Monte Carlo
approximation with particles. The proposed framework is validated using a
simulated oil spill incident in the Baltic sea with real ocean flow data. The
results show the efficacy of the developed spatio-temporal dispersion model and
estimation schemes in the presence of imperfect measurements. Moreover, the
parameter selection process is discussed, along with some comparison studies to
illustrate the advantages of the proposed algorithm over existing methods
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