15,183 research outputs found
Word-to-Word Models of Translational Equivalence
Parallel texts (bitexts) have properties that distinguish them from other
kinds of parallel data. First, most words translate to only one other word.
Second, bitext correspondence is noisy. This article presents methods for
biasing statistical translation models to reflect these properties. Analysis of
the expected behavior of these biases in the presence of sparse data predicts
that they will result in more accurate models. The prediction is confirmed by
evaluation with respect to a gold standard -- translation models that are
biased in this fashion are significantly more accurate than a baseline
knowledge-poor model. This article also shows how a statistical translation
model can take advantage of various kinds of pre-existing knowledge that might
be available about particular language pairs. Even the simplest kinds of
language-specific knowledge, such as the distinction between content words and
function words, is shown to reliably boost translation model performance on
some tasks. Statistical models that are informed by pre-existing knowledge
about the model domain combine the best of both the rationalist and empiricist
traditions
Models of Co-occurrence
A model of co-occurrence in bitext is a boolean predicate that indicates
whether a given pair of word tokens co-occur in corresponding regions of the
bitext space. Co-occurrence is a precondition for the possibility that two
tokens might be mutual translations. Models of co-occurrence are the glue that
binds methods for mapping bitext correspondence with methods for estimating
translation models into an integrated system for exploiting parallel texts.
Different models of co-occurrence are possible, depending on the kind of bitext
map that is available, the language-specific information that is available, and
the assumptions made about the nature of translational equivalence. Although
most statistical translation models are based on models of co-occurrence,
modeling co-occurrence correctly is more difficult than it may at first appear
Manual Annotation of Translational Equivalence: The Blinker Project
Bilingual annotators were paid to link roughly sixteen thousand corresponding
words between on-line versions of the Bible in modern French and modern
English. These annotations are freely available to the research community from
http://www.cis.upenn.edu/~melamed . The annotations can be used for several
purposes. First, they can be used as a standard data set for developing and
testing translation lexicons and statistical translation models. Second,
researchers in lexical semantics will be able to mine the annotations for
insights about cross-linguistic lexicalization patterns. Third, the annotations
can be used in research into certain recently proposed methods for monolingual
word-sense disambiguation. This paper describes the annotated texts, the
specially-designed annotation tool, and the strategies employed to increase the
consistency of the annotations. The annotation process was repeated five times
by different annotators. Inter-annotator agreement rates indicate that the
annotations are reasonably reliable and that the method is easy to replicate
Meaningfulness, the unsaid and translatability. Instead of an introduction
The present paper opens this topical issue on translation techniques by drawing a theoretical basis for the discussion of translational issues in a linguistic perspective. In order to forward an audience- oriented definition of translation, I will describe different forms of linguistic variability, highlighting how they present different difficulties to translators, with an emphasis on the semantic and communicative complexity that a source text can exhibit. The problem is then further discussed through a comparison between Quine's radically holistic position and the translatability principle supported by such semanticists as Katz. General translatability â at the expense of additional complexity â is eventually proposed as a possible synthesis of this debate. In describing the meaningfulness levels of source texts through Hjelmslevian semiotics, and his semiotic hierarchy in particular, the paper attempts to go beyond denotative semiotic, and reframe some translational issues in a connotative semiotic and metasemiotic perspective
Automatic Construction of Clean Broad-Coverage Translation Lexicons
Word-level translational equivalences can be extracted from parallel texts by
surprisingly simple statistical techniques. However, these techniques are
easily fooled by {\em indirect associations} --- pairs of unrelated words whose
statistical properties resemble those of mutual translations. Indirect
associations pollute the resulting translation lexicons, drastically reducing
their precision. This paper presents an iterative lexicon cleaning method. On
each iteration, most of the remaining incorrect lexicon entries are filtered
out, without significant degradation in recall. This lexicon cleaning technique
can produce translation lexicons with recall and precision both exceeding 90\%,
as well as dictionary-sized translation lexicons that are over 99\% correct.Comment: PostScript file, 10 pages. To appear in Proceedings of AMTA-9
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