521 research outputs found
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse
analysis. Computational Linguistics, 201
Kernel methods in machine learning
We review machine learning methods employing positive definite kernels. These
methods formulate learning and estimation problems in a reproducing kernel
Hilbert space (RKHS) of functions defined on the data domain, expanded in terms
of a kernel. Working in linear spaces of function has the benefit of
facilitating the construction and analysis of learning algorithms while at the
same time allowing large classes of functions. The latter include nonlinear
functions as well as functions defined on nonvectorial data. We cover a wide
range of methods, ranging from binary classifiers to sophisticated methods for
estimation with structured data.Comment: Published in at http://dx.doi.org/10.1214/009053607000000677 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Perfect simulation of autoregressive models with infinite memory
In this paper we consider the problem of determining the law of binary
stochastic processes from transition kernels depending on the whole past. These
kernels are linear in the past values of the process. They are allowed to
assume values close to both 0 and 1, preventing the application of usual
results on uniqueness. More precisely we give sufficient conditions for
uniqueness and non-uniqueness. In the former case a perfect simulation
algorithm is also given.Comment: 12 page
The average number of distinct sites visited by a random walker on random graphs
We study the linear large behavior of the average number of distinct
sites visited by a random walker after steps on a large random
graph. An expression for the graph topology dependent prefactor in is proposed. We use generating function techniques to relate this prefactor
to the graph adjacency matrix and then devise message-passing equations to
calculate its value. Numerical simulations are performed to evaluate the
agreement between the message passing predictions and random walk simulations
on random graphs. Scaling with system size and average graph connectivity are
also analysed.Comment: 22 pages, 4 figure
On-the-fly reduction of open loops
Building on the open-loop algorithm we introduce a new method for the
automated construction of one-loop amplitudes and their reduction to scalar
integrals. The key idea is that the factorisation of one-loop integrands in a
product of loop segments makes it possible to perform various operations
on-the-fly while constructing the integrand. Reducing the integrand on-the-fly,
after each segment multiplication, the construction of loop diagrams and their
reduction are unified in a single numerical recursion. In this way we entirely
avoid objects with high tensor rank, thereby reducing the complexity of the
calculations in a drastic way. Thanks to the on-the-fly approach, which is
applied also to helicity summation and for the merging of different diagrams,
the speed of the original open-loop algorithm can be further augmented in a
very significant way. Moreover, addressing spurious singularities of the
employed reduction identities by means of simple expansions in rank-two Gram
determinants, we achieve a remarkably high level of numerical stability. These
features of the new algorithm, which will be made publicly available in a
forthcoming release of the OpenLoops program, are particularly attractive for
NLO multi-leg and NNLO real-virtual calculations.Comment: v2 as accepted by EPJ C: extended discussion of the triangle
reduction and its numerical stability in section 5.4.2; speed benchmarks for
2->5 processes included in section 6.2.1; ref. adde
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