DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation
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DOI：
10.18653/v1/2021.acl-long.501
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Xinyu Hua;Ashwin Sreevatsa;Lu Wang
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文献类型：
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作者：
Xinyu Hua;Ashwin Sreevatsa;Lu Wang
我们研究的任务，长形式的意见文本生成，它面临着至少两个不同的挑战。首先，现有的神经生成模型缺乏一致性，因此需要有效的内容规划。其次，需要不同类型的信息来指导生成器涵盖主观和客观内容。为此，我们提出DYPLOC，一个生成框架，进行动态规划的内容，同时生成的输出基于一个新的设计的混合语言模型。为了丰富具有多样化内容的生成，我们进一步建议使用大型预训练模型来预测相关概念并生成声明。我们在新收集的数据集上进行了两项具有挑战性的任务：（1）使用Reddit ChangeMyView生成参数，以及（2）使用纽约时报的观点部分撰写文章。自动评估表明，我们的模型显着优于竞争力的比较。人类法官进一步证实，我们这几代人更有连贯性，内容更丰富。
We study the task of long-form opinion text generation, which faces at least two distinct challenges. First, existing neural generation models fall short of coherence, thus requiring efficient content planning. Second, diverse types of information are needed to guide the generator to cover both subjective and objective content. To this end, we propose DYPLOC, a generation framework that conducts dynamic planning of content while generating the output based on a novel design of mixed language models. To enrich the generation with diverse content, we further propose to use large pre-trained models to predict relevant concepts and to generate claims. We experiment with two challenging tasks on newly collected datasets: (1) argument generation with Reddit ChangeMyView, and (2) writing articles using New York Times’ Opinion section. Automatic evaluation shows that our model significantly outperforms competitive comparisons. Human judges further confirm that our generations are more coherent with richer content.