Neural paraphrasing by automatically crawled and aligned sentence pairs
Achille Globo Antonio Trevisi Andrea Zugarini Leonardo Rigutini Marco Maggini Stefano Melacci
Paraphrasing is the task of re-writing an input text using other words,
without altering the meaning of the original content. Conversational systems
can exploit automatic paraphrasing to make the conversation more natural, e.g.,
talking about a certain topic using different paraphrases in different time
instants. Recently, the task of automatically generating paraphrases has been
approached in the context of Natural Language Generation (NLG). While many
existing systems simply consist in rule-based models, the recent success of the
Deep Neural Networks in several NLG tasks naturally suggests the possibility of
exploiting such networks for generating paraphrases. However, the main obstacle
toward neural-network-based paraphrasing is the lack of large datasets with
aligned pairs of sentences and paraphrases, that are needed to efficiently
train the neural models. In this paper we present a method for the automatic
generation of large aligned corpora, that is based on the assumption that news
and blog websites talk about the same events using different narrative styles.
We propose a similarity search procedure with linguistic constraints that,
given a reference sentence, is able to locate the most similar candidate
paraphrases out from millions of indexed sentences. The data generation process
is evaluated in the case of the Italian language, performing experiments using
pointer-based deep neural architectures.