2,817 research outputs found
Paraphrasing and Translation
Paraphrasing and translation have previously been treated as unconnected natural lan¬
guage processing tasks. Whereas translation represents the preservation of meaning
when an idea is rendered in the words in a different language, paraphrasing represents
the preservation of meaning when an idea is expressed using different words in the
same language. We show that the two are intimately related. The major contributions
of this thesis are as follows:• We define a novel technique for automatically generating paraphrases using
bilingual parallel corpora, which are more commonly used as training data for
statistical models of translation.• We show that paraphrases can be used to improve the quality of statistical ma¬
chine translation by addressing the problem of coverage and introducing a degree
of generalization into the models.• We explore the topic of automatic evaluation of translation quality, and show that
the current standard evaluation methodology cannot be guaranteed to correlate
with human judgments of translation quality.Whereas previous data-driven approaches to paraphrasing were dependent upon
either data sources which were uncommon such as multiple translation of the same
source text, or language specific resources such as parsers, our approach is able to
harness more widely parallel corpora and can be applied to any language which has
a parallel corpus. The technique was evaluated by replacing phrases with their para¬
phrases, and asking judges whether the meaning of the original phrase was retained
and whether the resulting sentence remained grammatical. Paraphrases extracted from
a parallel corpus with manual alignments are judged to be accurate (both meaningful
and grammatical) 75% of the time, retaining the meaning of the original phrase 85%
of the time. Using automatic alignments, meaning can be retained at a rate of 70%.Being a language independent and probabilistic approach allows our method to be
easily integrated into statistical machine translation. A paraphrase model derived from
parallel corpora other than the one used to train the translation model can be used to
increase the coverage of statistical machine translation by adding translations of previously unseen words and phrases. If the translation of a word was not learned, but
a translation of a synonymous word has been learned, then the word is paraphrased and its paraphrase is translated. Phrases can be treated similarly. Results show that
augmenting a state-of-the-art SMT system with paraphrases in this way leads to significantly improved coverage and translation quality. For a training corpus with 10,000
sentence pairs, we increase the coverage of unique test set unigrams from 48% to 90%,
with more than half of the newly covered items accurately translated, as opposed to
none in current approaches
Generalization of graph network inferences in higher-order probabilistic graphical models
Probabilistic graphical models provide a powerful tool to describe complex
statistical structure, with many real-world applications in science and
engineering from controlling robotic arms to understanding neuronal
computations. A major challenge for these graphical models is that inferences
such as marginalization are intractable for general graphs. These inferences
are often approximated by a distributed message-passing algorithm such as
Belief Propagation, which does not always perform well on graphs with cycles,
nor can it always be easily specified for complex continuous probability
distributions. Such difficulties arise frequently in expressive graphical
models that include intractable higher-order interactions. In this paper we
construct iterative message-passing algorithms using Graph Neural Networks
defined on factor graphs to achieve fast approximate inference on graphical
models that involve many-variable interactions. Experimental results on several
families of graphical models demonstrate the out-of-distribution generalization
capability of our method to different sized graphs, and indicate the domain in
which our method gains advantage over Belief Propagation.Comment: 9 pages, 2 figure
- …