论文标题
不要改变我！用户控制的选择性释义生成
Don't Change Me! User-Controllable Selective Paraphrase Generation
论文作者
论文摘要
在释义生成任务中，源句子通常包含不应更改的短语。但是，哪个短语可以取决于上下文，并且可以因应用而变化。我们应对这一挑战的解决方案是为用户提供明确的标签，这些标签可以围绕任何任意的文本段放置，以表示“不要改变我！”产生释义时；该模型学会了将这些短语显式复制到输出。这项工作的贡献是一种使用遥远监督的新型数据生成技术，该技术使我们可以从审慎的序列到序列模型开始，并微调一种释义生成器，表现出这种行为，从而允许用户控制的释义生成。此外，我们修改了微调过程中的损失，以明确鼓励模型输出的多样性。我们的技术是语言不可知论，我们报告了英语和中文的实验。
In the paraphrase generation task, source sentences often contain phrases that should not be altered. Which phrases, however, can be context dependent and can vary by application. Our solution to this challenge is to provide the user with explicit tags that can be placed around any arbitrary segment of text to mean "don't change me!" when generating a paraphrase; the model learns to explicitly copy these phrases to the output. The contribution of this work is a novel data generation technique using distant supervision that allows us to start with a pretrained sequence-to-sequence model and fine-tune a paraphrase generator that exhibits this behavior, allowing user-controllable paraphrase generation. Additionally, we modify the loss during fine-tuning to explicitly encourage diversity in model output. Our technique is language agnostic, and we report experiments in English and Chinese.
