82 research outputs found
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation
Recent studies have revealed a number of pathologies of neural machine
translation (NMT) systems. Hypotheses explaining these mostly suggest that
there is something fundamentally wrong with NMT as a model or its training
algorithm, maximum likelihood estimation (MLE). Most of this evidence was
gathered using maximum a posteriori (MAP) decoding, a decision rule aimed at
identifying the highest-scoring translation, i.e. the mode, under the model
distribution. We argue that the evidence corroborates the inadequacy of MAP
decoding more than casts doubt on the model and its training algorithm. In this
work, we criticise NMT models probabilistically showing that stochastic samples
following the model's own generative story do reproduce various statistics of
the training data well, but that it is beam search that strays from such
statistics. We show that some of the known pathologies of NMT are due to MAP
decoding and not to NMT's statistical assumptions nor MLE. In particular, we
show that the most likely translations under the model accumulate so little
probability mass that the mode can be considered essentially arbitrary. We
therefore advocate for the use of decision rules that take into account
statistics gathered from the model distribution holistically. As a proof of
concept we show that a straightforward implementation of minimum Bayes risk
decoding gives good results outperforming beam search using as little as 30
samples, confirming that MLE-trained NMT models do capture important aspects of
translation well in expectation
Auto-Encoding Variational Neural Machine Translation
We present a deep generative model of bilingual sentence pairs for machine
translation. The model generates source and target sentences jointly from a
shared latent representation and is parameterised by neural networks. We
perform efficient training using amortised variational inference and
reparameterised gradients. Additionally, we discuss the statistical
implications of joint modelling and propose an efficient approximation to
maximum a posteriori decoding for fast test-time predictions. We demonstrate
the effectiveness of our model in three machine translation scenarios:
in-domain training, mixed-domain training, and learning from a mix of
gold-standard and synthetic data. Our experiments show consistently that our
joint formulation outperforms conditional modelling (i.e. standard neural
machine translation) in all such scenarios
Direct frequency comb spectroscopy of trapped ions
Direct frequency comb spectroscopy of trapped ions is demonstated for the
first time. It is shown that the 4s^2S_(1/2)-4p^2P_(3/2) transition in calcium
ions can be excited directly with a frequency comb laser that is upconverted to
393 nm. Detection of the transition is performed using a shelving scheme to
suppress background signal from non-resonant comb modes. The measured
transition frequency of f=761 905 012.7(0.5) MHz presents an improvement in
accuracy of more than two orders of magnitude.Comment: 4 pages, 5 figur
Frequency metrology on the 4s 2S1/2 - 4p 2P1/2 transition in the calcium ion for a comparison with quasar data
High accuracy frequency metrology on the 4s 2S1/2 - 4p 2P1/2 transition in
calcium ions is performed using laser cooled and crystallized ions in a linear
Paul trap. Calibration is performed with a frequency comb laser, resulting in a
transition frequency of f=755222766.2(1.7) MHz. The accuracy presents an
improvement of more than one order of magnitude, and will facilitate a
comparison with quasar data in a search for a possible change of the fine
structure constant on a cosmological time scale.Comment: Corrected typos (including one on the axis of figure 6
Ion distribution and ablation depth measurements of a fs-ps laser-irradiated solid tin target
The ablation of solid tin surfaces by an 800-nanometer-wavelength laser is
studied for a pulse length range from 500 fs to 4.5 ps and a fluence range
spanning 0.9 to 22 J/cm^2. The ablation depth and volume are obtained employing
a high-numerical-aperture optical microscope, while the ion yield and energy
distributions are obtained from a set of Faraday cups set up under various
angles. We found a slight increase of the ion yield for an increasing pulse
length, while the ablation depth is slightly decreasing. The ablation volume
remained constant as a function of pulse length. The ablation depth follows a
two-region logarithmic dependence on the fluence, in agreement with the
available literature and theory. In the examined fluence range, the ion yield
angular distribution is sharply peaked along the target normal at low fluences
but rapidly broadens with increasing fluence. The total ionization fraction
increases monotonically with fluence to a 5-6% maximum, which is substantially
lower than the typical ionization fractions obtained with nanosecond-pulse
ablation. The angular distribution of the ions does not depend on the laser
pulse length within the measurement uncertainty. These results are of
particular interest for the possible utilization of fs-ps laser systems in
plasma sources of extreme ultraviolet light for nanolithography.Comment: 8 pages, 7 figure
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