1,571 research outputs found
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Using same-language machine translation to create alternative target sequences for text-to-speech synthesis
Modern speech synthesis systems attempt to produce
speech utterances from an open domain of words. In some situations, the synthesiser will not have the appropriate units to pronounce some words or phrases accurately but it still must attempt to pronounce them. This paper presents a hybrid machine translation and unit selection speech synthesis system. The machine translation system was trained with English as the source and target language. Rather than the synthesiser only saying the input text as would happen in conventional synthesis systems, the synthesiser may say an alternative utterance with the same
meaning. This method allows the synthesiser to overcome the
problem of insufficient units in runtime
A Continuously Growing Dataset of Sentential Paraphrases
A major challenge in paraphrase research is the lack of parallel corpora. In
this paper, we present a new method to collect large-scale sentential
paraphrases from Twitter by linking tweets through shared URLs. The main
advantage of our method is its simplicity, as it gets rid of the classifier or
human in the loop needed to select data before annotation and subsequent
application of paraphrase identification algorithms in the previous work. We
present the largest human-labeled paraphrase corpus to date of 51,524 sentence
pairs and the first cross-domain benchmarking for automatic paraphrase
identification. In addition, we show that more than 30,000 new sentential
paraphrases can be easily and continuously captured every month at ~70%
precision, and demonstrate their utility for downstream NLP tasks through
phrasal paraphrase extraction. We make our code and data freely available.Comment: 11 pages, accepted to EMNLP 201
The Circle of Meaning: From Translation to Paraphrasing and Back
The preservation of meaning between inputs and outputs is perhaps
the most ambitious and, often, the most elusive goal of systems
that attempt to process natural language. Nowhere is this goal of
more obvious importance than for the tasks of machine translation
and paraphrase generation. Preserving meaning between the input and
the output is paramount for both, the monolingual vs bilingual distinction
notwithstanding. In this thesis, I present a novel, symbiotic relationship
between these two tasks that I term the "circle of meaning''.
Today's statistical machine translation (SMT) systems require high
quality human translations for parameter tuning, in addition to
large bi-texts for learning the translation units. This parameter
tuning usually involves generating translations at different points
in the parameter space and obtaining feedback against human-authored
reference translations as to how good the translations. This feedback
then dictates what point in the parameter space should be explored
next. To measure this feedback, it is generally considered wise to have
multiple (usually 4) reference translations to avoid unfair penalization of translation
hypotheses which could easily happen given the large number of ways in which
a sentence can be translated from one language to another. However, this reliance on multiple reference translations
creates a problem since they are labor intensive and expensive to obtain.
Therefore, most current MT datasets only contain a single reference.
This leads to the problem of reference sparsity---the primary open problem
that I address in this dissertation---one that has a serious effect on the
SMT parameter tuning process.
Bannard and Callison-Burch (2005) were the first to provide a practical
connection between phrase-based statistical machine translation and paraphrase
generation. However, their technique is restricted to generating phrasal
paraphrases. I build upon their approach and augment a phrasal paraphrase
extractor into a sentential paraphraser with extremely broad coverage.
The novelty in this augmentation lies in the further strengthening of
the connection between statistical machine translation and paraphrase
generation; whereas Bannard and Callison-Burch only relied on SMT machinery
to extract phrasal paraphrase rules and stopped there, I take it a few
steps further and build a full English-to-English SMT system. This system
can, as expected, ``translate'' any English input sentence into a new English
sentence with the same degree of meaning preservation that exists in a bilingual
SMT system. In fact, being a state-of-the-art SMT system, it is able to generate
n-best "translations" for any given input sentence. This sentential
paraphraser, built almost entirely from existing SMT machinery, represents
the first 180 degrees of the circle of meaning.
To complete the circle, I describe a novel connection in the other direction.
I claim that the sentential paraphraser, once built in this fashion, can
provide a solution to the reference sparsity problem and, hence, be used
to improve the performance a bilingual SMT system. I discuss two different
instantiations of the sentential paraphraser and show several results that
provide empirical validation for this connection
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
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