19,833 research outputs found
Style Transfer in Text: Exploration and Evaluation
Style transfer is an important problem in natural language processing (NLP).
However, the progress in language style transfer is lagged behind other
domains, such as computer vision, mainly because of the lack of parallel data
and principle evaluation metrics. In this paper, we propose to learn style
transfer with non-parallel data. We explore two models to achieve this goal,
and the key idea behind the proposed models is to learn separate content
representations and style representations using adversarial networks. We also
propose novel evaluation metrics which measure two aspects of style transfer:
transfer strength and content preservation. We access our models and the
evaluation metrics on two tasks: paper-news title transfer, and
positive-negative review transfer. Results show that the proposed content
preservation metric is highly correlate to human judgments, and the proposed
models are able to generate sentences with higher style transfer strength and
similar content preservation score comparing to auto-encoder.Comment: To appear in AAAI-1
Large-scale Hierarchical Alignment for Data-driven Text Rewriting
We propose a simple unsupervised method for extracting pseudo-parallel
monolingual sentence pairs from comparable corpora representative of two
different text styles, such as news articles and scientific papers. Our
approach does not require a seed parallel corpus, but instead relies solely on
hierarchical search over pre-trained embeddings of documents and sentences. We
demonstrate the effectiveness of our method through automatic and extrinsic
evaluation on text simplification from the normal to the Simple Wikipedia. We
show that pseudo-parallel sentences extracted with our method not only
supplement existing parallel data, but can even lead to competitive performance
on their own.Comment: RANLP 201
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