论文标题
沿情感梯度的细粒度情绪释义
Fine-Grained Emotional Paraphrasing along Emotion Gradients
论文作者
论文摘要
术产生，又名释义，是自然语言处理中的常见和重要任务。情感释义会改变文本中体现的情绪，同时保留其含义，具有许多潜在的应用，例如，调节在线对话并防止网络欺凌。我们介绍了一项新的任务，即沿情感梯度进行细颗粒的情感释义，即，随着情感维度的平稳变化，在保留原始作品的含义的同时，在细晶粒中改变了细晶粒的情感强度。我们提出了一个框架，通过通过多任务培训微调文本到文本变压器来解决此任务。我们通过注释输入和目标文本，并使用其细粒度的情感标签来增强几种广泛使用的释义语料库。使用这些标签，这些语料库上的微调文本到文本变压器需要多任务培训。 Evaluations of the fine-tuned Transformers on separate test sets show that including fine-grained emotion labels in the paraphrase task significantly improve the chance of obtaining high-quality paraphrases of the desired emotions, i.e., more than doubling the number of exact matches of desired emotions while achieving consistently better scores in paraphrase metrics such as BLEU, ROGUE, and METEOR.
Paraphrase generation, a.k.a. paraphrasing, is a common and important task in natural language processing. Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has many potential applications, e.g., moderating online dialogues and preventing cyberbullying. We introduce a new task of fine-grained emotional paraphrasing along emotion gradients, that is, altering the emotional intensities of the paraphrases in fine grain following smooth variations in affective dimensions while preserving the meanings of the originals. We propose a framework for addressing this task by fine-tuning text-to-text Transformers through multi-task training. We enhance several widely used paraphrasing corpus by annotating the input and target texts with their fine-grained emotion labels. With these labels, fine-tuning text-to-text Transformers on these corpus entails multi-task training. Evaluations of the fine-tuned Transformers on separate test sets show that including fine-grained emotion labels in the paraphrase task significantly improve the chance of obtaining high-quality paraphrases of the desired emotions, i.e., more than doubling the number of exact matches of desired emotions while achieving consistently better scores in paraphrase metrics such as BLEU, ROGUE, and METEOR.
