嵌入约束的商业推文释义生成

Renhao Cui, G. Agrawal, R. Ramnath
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引用次数: 1

摘要

自动生成商业推文已经成为利用社交媒体进行营销和广告的一个有用和重要的工具。在这种背景下,释义生成已成为一个重要的问题。这种类型的释义生成具有独特的需求,要求在结果中保留某些元素,例如产品名称或促销细节。为了解决这一需求,我们提出了一个约束嵌入式语言建模(CELM)框架,其中硬约束嵌入到文本内容中,并通过语言模型学习。这种嵌入不仅可以帮助模型学习释义生成,还可以学习特定于商业推文的释义内容的约束。此外,我们将从一般领域学习到的知识应用于商业推文的生成任务。我们的模型在释义相似性、多样性和符合硬约束的能力方面,表现优于一般的释义生成模型以及最先进的CopyNet模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constraint-embedded paraphrase generation for commercial tweets
Automated generation of commercial tweets has become a useful and important tool in the use of social media for marketing and advertising. In this context, paraphrase generation has emerged as an important problem. This type of paraphrase generation has the unique requirement of requiring certain elements to be kept in the result, such as the product name or the promotion details. To address this need, we propose a Constraint-Embedded Language Modeling (CELM) framework, in which hard constraints are embedded in the text content and learned through a language model. This embedding helps the model learn not only paraphrase generation but also constraints in the content of the paraphrase specific to commercial tweets. In addition, we apply knowledge learned from a general domain to the generation task of commercial tweets. Our model is shown to outperform general paraphrase generation models as well as the state-of-the-art CopyNet model, in terms of paraphrase similarity, diversity, and the ability to conform to hard constraints.
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