54 research outputs found
Controlling Linguistic Style Aspects in Neural Language Generation
Most work on neural natural language generation (NNLG) focus on controlling
the content of the generated text. We experiment with controlling several
stylistic aspects of the generated text, in addition to its content. The method
is based on conditioned RNN language model, where the desired content as well
as the stylistic parameters serve as conditioning contexts. We demonstrate the
approach on the movie reviews domain and show that it is successful in
generating coherent sentences corresponding to the required linguistic style
and content
Evaluating prose style transfer with the Bible
In the prose style transfer task a system, provided with text input and a
target prose style, produces output which preserves the meaning of the input
text but alters the style. These systems require parallel data for evaluation
of results and usually make use of parallel data for training. Currently, there
are few publicly available corpora for this task. In this work, we identify a
high-quality source of aligned, stylistically distinct text in different
versions of the Bible. We provide a standardized split, into training,
development and testing data, of the public domain versions in our corpus. This
corpus is highly parallel since many Bible versions are included. Sentences are
aligned due to the presence of chapter and verse numbers within all versions of
the text. In addition to the corpus, we present the results, as measured by the
BLEU and PINC metrics, of several models trained on our data which can serve as
baselines for future research. While we present these data as a style transfer
corpus, we believe that it is of unmatched quality and may be useful for other
natural language tasks as well
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