8 research outputs found
Estimating Maximally Probable Constrained Relations by Mathematical Programming
Estimating a constrained relation is a fundamental problem in machine
learning. Special cases are classification (the problem of estimating a map
from a set of to-be-classified elements to a set of labels), clustering (the
problem of estimating an equivalence relation on a set) and ranking (the
problem of estimating a linear order on a set). We contribute a family of
probability measures on the set of all relations between two finite, non-empty
sets, which offers a joint abstraction of multi-label classification,
correlation clustering and ranking by linear ordering. Estimating (learning) a
maximally probable measure, given (a training set of) related and unrelated
pairs, is a convex optimization problem. Estimating (inferring) a maximally
probable relation, given a measure, is a 01-linear program. It is solved in
linear time for maps. It is NP-hard for equivalence relations and linear
orders. Practical solutions for all three cases are shown in experiments with
real data. Finally, estimating a maximally probable measure and relation
jointly is posed as a mixed-integer nonlinear program. This formulation
suggests a mathematical programming approach to semi-supervised learning.Comment: 16 page
The Linear Ordering Problem Revisited
The Linear Ordering Problem is a popular combinatorial optimisation problem which has been extensively addressed in the literature. However, in spite of its popularity, little is known about the characteristics of this problem. This paper studies a procedure to extract static information from an instance of the problem, and proposes a method to incorporate the obtained knowledge in order to improve the performance of local search-based algorithms. The procedure introduced identifies the positions where the indexes cannot generate local optima for the insert neighbourhood, and thus global optima solutions. This information is then used to propose a restricted insert neighbourhood that discards the insert operations which move indexes to positions where optimal solutions are not generated.
In order to measure the efficiency of the proposed restricted insert neighbourhood system, two state-of-the-art algorithms for the LOP that include local search procedures have been modified. Conducted experiments confirm that the restricted versions of the algorithms outperform the classical designs systematically. The statistical test included in the experimentation reports significant differences in all the cases, which validates the efficiency of our proposal
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine
translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the
community in this problem has not decreased, and no single method appears to be
strongly dominant across language pairs. Instead, the choice of the optimal
approach for a new translation task still seems to be mostly driven by
empirical trials. To orientate the reader in this vast and complex research
area, we present a comprehensive survey of word reordering viewed as a
statistical modeling challenge and as a natural language phenomenon. The survey
describes in detail how word reordering is modeled within different
string-based and tree-based SMT frameworks and as a stand-alone task, including
systematic overviews of the literature in advanced reordering modeling. We then
question why some approaches are more successful than others in different
language pairs. We argue that, besides measuring the amount of reordering, it
is important to understand which kinds of reordering occur in a given language
pair. To this end, we conduct a qualitative analysis of word reordering
phenomena in a diverse sample of language pairs, based on a large collection of
linguistic knowledge. Empirical results in the SMT literature are shown to
support the hypothesis that a few linguistic facts can be very useful to
anticipate the reordering characteristics of a language pair and to select the
SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic
Syntax-based machine translation using dependency grammars and discriminative machine learning
Machine translation underwent huge improvements since the groundbreaking
introduction of statistical methods in the early 2000s, going from very
domain-specific systems that still performed relatively poorly despite the
painstakingly crafting of thousands of ad-hoc rules, to general-purpose
systems automatically trained on large collections of bilingual texts which
manage to deliver understandable translations that convey the general
meaning of the original input.
These approaches however still perform quite below the level of human
translators, typically failing to convey detailed meaning and register, and
producing translations that, while readable, are often ungrammatical and
unidiomatic.
This quality gap, which is considerably large compared to most other
natural language processing tasks, has been the focus of the research in
recent years, with the development of increasingly sophisticated models that
attempt to exploit the syntactical structure of human languages, leveraging
the technology of statistical parsers, as well as advanced machine learning
methods such as marging-based structured prediction algorithms and neural
networks.
The translation software itself became more complex in order to accommodate
for the sophistication of these advanced models: the main translation
engine (the decoder) is now often combined with a pre-processor which
reorders the words of the source sentences to a target language word order, or
with a post-processor that ranks and selects a translation according according
to fine model from a list of candidate translations generated by a coarse
model.
In this thesis we investigate the statistical machine translation problem
from various angles, focusing on translation from non-analytic languages
whose syntax is best described by fluid non-projective dependency grammars
rather than the relatively strict phrase-structure grammars or projectivedependency
grammars which are most commonly used in the literature.
We propose a framework for modeling word reordering phenomena
between language pairs as transitions on non-projective source dependency
parse graphs. We quantitatively characterize reordering phenomena for the
German-to-English language pair as captured by this framework, specifically
investigating the incidence and effects of the non-projectivity of source
syntax and the non-locality of word movement w.r.t. the graph structure.
We evaluated several variants of hand-coded pre-ordering rules in order to
assess the impact of these phenomena on translation quality.
We propose a class of dependency-based source pre-ordering approaches
that reorder sentences based on a flexible models trained by SVMs and and
several recurrent neural network architectures.
We also propose a class of translation reranking models, both syntax-free
and source dependency-based, which make use of a type of neural networks
known as graph echo state networks which is highly flexible and requires
extremely little training resources, overcoming one of the main limitations
of neural network models for natural language processing tasks
Learning Linear Ordering Problems for Better Translation ∗
We apply machine learning to the Linear Ordering Problem in order to learn sentence-specific reordering models for machine translation. We demonstrate that even when these models are used as a mere preprocessing step for German-English translation, they significantly outperform Moses ’ integrated lexicalized reordering model. Our models are trained on automatically aligned bitext. Their form is simple but novel. They assess, based on features of the input sentence, how strongly each pair of input word tokens wi, wj would like to reverse their relative order. Combining all these pairwise preferences to find the best global reordering is NP-hard. However, we present a non-trivial O(n3) algorithm, based on chart parsing, that at least finds the best reordering within a certain exponentially large neighborhood. We show how to iterate this reordering process within a local search algorithm, which we use in training.