۲۹ بهمن ۱۴۰۳
رضوان محمدي باغملايي

رضوان محمدی باغملایی

مرتبه علمی: استادیار
نشانی: دانشکده مهندسی سیستم های هوشمند و علوم داده - گروه مهندسی کامپیوتر
تحصیلات: دکترای تخصصی / هوش مصنوعی
تلفن: --
دانشکده: دانشکده مهندسی سیستم های هوشمند و علوم داده

مشخصات پژوهش

عنوان TET: Text emotion transfer
نوع پژوهش مقالات در نشریات
کلیدواژه‌ها
Text style transfer Emotion recognition Transfer learning Masked language modeling Transformers
مجله KNOWLEDGE-BASED SYSTEMS
شناسه DOI https://doi.org/10.1016/j.knosys.2022.110236
پژوهشگران رضوان محمدی باغملایی (نفر اول) ، علی احمدی (نفر دوم)

چکیده

Text style transfer aims at transforming the style of a piece of text while keeping its primary content. The style of the text is usually defined as a particular writing tone in different categories, such as formality, politeness, sentiment, and political slant. Recently, most of the work in the area has been devoted to the problem of sentiment transfer, which tries to transfer an opinionated text into a positive or negative perspective. It has applications in marketing, political news, chatbots, writing tools, and many others. On the other hand, emotions as the basic forms of sentiments have brought many attentions to different tasks, including image style transfer but they are not well expressed in text style transfer yet. This article presents a text emotion transfer model that transforms the style of a text to each of the predefined ‘anger’, ‘fear’, ‘joy’, and ‘sadness’ emotions. Relying on masked language modeling and transfer learning, the proposed model can perform efficiently on limited amounts of emotion-annotated data. Moreover, the model shows promising experimental results against other existing models considering style transfer accuracy, content preservation, and fluency in the ISEAR and TEC emotion corpora.