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Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories

机译:混合的感觉:带有可变,共存情感类别的自然文本生成

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Conversational agents, having the goal of natural language generation, must rely on language models which can integrate emotion into their responses. Recent projects outline models which can produce emotional sentences, but unlike human language, they tend to be restricted to one affective category out of a few (e.g. Zhao et al. (2018)). To my knowledge, none allow for the intentional coexistence of multiple emotions on the word or sentence level. Building on prior research which allows for variation in the intensity of a singular emotion (Ghosh et al., 2017), this research proposal outlines an LSTM (Long Short-Term Memory) language model which allows for variation in multiple emotions simultaneously.
机译:以自然语言生成为目标的对话主体必须依靠可以将情感整合到其响应中的语言模型。最近的项目概述了可以产生情感句子的模型,但与人类语言不同的是,它们往往被限制在少数几个情感类别中(例如Zhao等人(2018))。据我所知,没有一个允许在单词或句子层面上多种情感的故意共存。该研究建议以先前的研究为基础,该研究允许改变单个情感的强度(Ghosh等人,2017),概述了LSTM(长期短期记忆)语言模型,该模型允许同时改变多个情感。

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