We propose the task of Quantifiable Sequence Editing (QuaSE): editing an
input sequence to generate an output sequence that satisfies a given numerical
outcome value measuring a certain property of the sequence, with the
requirement of keeping the main content of the input sequence. For example, an
input sequence could be a word sequence, such as review sentence and
advertisement text. For a review sentence, the outcome could be the review
rating; for an advertisement, the outcome could be the click-through rate. The
major challenge in performing QuaSE is how to perceive the outcome-related
wordings, and only edit them to change the outcome. In this paper, the proposed
framework contains two latent factors, namely, outcome factor and content
factor, disentangled from the input sentence to allow convenient editing to
change the outcome and keep the content. Our framework explores the
pseudo-parallel sentences by modeling their content similarity and outcome
differences to enable a better disentanglement of the latent factors, which
allows generating an output to better satisfy the desired outcome and keep the
content. The dual reconstruction structure further enhances the capability of
generating expected output by exploiting the couplings of latent factors of
pseudo-parallel sentences. For evaluation, we prepared a dataset of Yelp review
sentences with the ratings as outcome. Extensive experimental results are
reported and discussed to elaborate the peculiarities of our framework.

本文提出了一种 Quantifiable Sequence Editing（QuaSE）任务，通过两个潜在的因素 (disentangled factors)：outcome factor 和 content factor 实现只编辑与 outcome value 有关的单词，从而更好地满足所需的 outcome value 并保持内容不变。该方法在 Yelp 评论数据上进行了评估，并取得了良好的实验结果。