We propose the task of emotion style transfer, which is particularly
challenging, as emotions (here: anger, disgust, fear, joy, sadness, surprise)
are on the fence between content and style. To understand the particular
difficulties of this task, we design a transparent emotion style transfer
pipeline based on three steps: (1) select the words that are promising to be
substituted to change the emotion (with a brute-force approach and selection
based on the attention mechanism of an emotion classifier), (2) find sets of
words as candidates for substituting the words (based on lexical and
distributional semantics), and (3) select the most promising combination of
substitutions with an objective function which consists of components for
content (based on BERT sentence embeddings), emotion (based on an emotion
classifier), and fluency (based on a neural language model). This comparably
straight-forward setup enables us to explore the task and understand in what
cases lexical substitution can vary the emotional load of texts, how changes in
content and style interact and if they are at odds. We further evaluate our
pipeline quantitatively in an automated and an annotation study based on Tweets
and find, indeed, that simultaneous adjustments of content and emotion are
conflicting objectives: as we show in a qualitative analysis motivated by
Scherer's emotion component model, this is particularly the case for implicit
emotion expressions based on cognitive appraisal or descriptions of bodily
reactions.

我们提出了情感风格转移的任务，并设计了基于三个步骤的透明情感风格转移流程，其中包括选择最有前途的单词进行替换，使用词汇和分布语义找到替代单词的候选集以及使用包括内容、情感和流畅性等组成的客观函数选择最有前途的替代组合。通过自动化和注释研究，在推特上进行量化评估，发现调整内容和情感的同时是冲突的目标。