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Nonparallel Emotional Speech Conversion
We propose a nonparallel data-driven emotional speech conversion method....
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An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs
The effect of amplifiers, downtoners, and negations has been studied in ...
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Parameter-Free Style Projection for Arbitrary Style Transfer
Arbitrary image style transfer is a challenging task which aims to styli...
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Low-Level Linguistic Controls for Style Transfer and Content Preservation
Despite the success of style transfer in image processing, it has seen l...
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Review of text style transfer based on deep learning
Text style transfer is a hot issue in recent natural language processing...
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Multi-Reference Neural TTS Stylization with Adversarial Cycle Consistency
Current multi-reference style transfer models for Text-to-Speech (TTS) p...
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JeSemE: A Website for Exploring Diachronic Changes in Word Meaning and Emotion
We here introduce a substantially extended version of JeSemE, a website ...
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Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution Pipeline
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.
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