论文标题
双向生成专利语言模型的有效性
The Effectiveness of Bidirectional Generative Patent Language Models
论文作者
论文摘要
生成的专利语言模型可以帮助人类更有效地编写专利文本。问题是如何从以人为中心的角度衡量有效性以及如何提高效力。在本手稿中，提出了简化的自动完成功能的设计，以提高有效性超过10％。有了新的设计，自动填充的有效性可以达到60％以上，这意味着可以通过自动完成来保存超过60％的击键。由于编写专利文本并不一定从头到尾开始，因此问题是生成模型是否可以无论在哪里开始写作都可以帮助用户。为了回答这个问题，本手稿中的生成模型都通过两个方向的训练数据进行了预先培训。生成模型成为双向。由于文本的生成是双向的，因此自动完成有效性的计算可以是双向的，并从文本中的任何地方开始。经过彻底的实验，一个关键发现是，无论计算何时启动计算，模型的自动完成效率仍然相似。该发现表明，无论用户在何处编写何处，这种双向模型都可以在类似级别上为用户提供帮助。
Generative patent language models can assist humans to write patent text more effectively. The question is how to measure effectiveness from a human-centric perspective and how to improve effectiveness. In this manuscript, a simplified design of the autocomplete function is proposed to increase effectiveness by more than 10%. With the new design, the effectiveness of autocomplete can reach more than 60%, which means that more than 60% of keystrokes can be saved by autocomplete. Since writing patent text does not necessarily start from the beginning to the end, a question is whether the generative model can assist a user no matter where to start writing. To answer the question, the generative models in this manuscript are pre-trained with training data in both directions. The generative models become bidirectional. Since text generation is bidirectional, the calculation of autocomplete effectiveness can be bidirectional and starts from anywhere in the text. After thorough experiments, a key finding is that the autocomplete effectiveness of a model for the same text remains similar no matter where the calculation starts. The finding indicates that such bidirectional models can assist a user at a similar level, no matter where the user starts to write.
