论文标题
统一的伯特（Bert
Unified BERT for Few-shot Natural Language Understanding
论文作者
论文摘要
即使预训练的语言模型共享语义编码器，自然语言的理解也遭受了各种输出模式的影响。在本文中，我们提出了基于BERT框架的统一双向语言理解模型Ubert，它可以通过Biaffine网络普遍地对不同NLU任务的训练对象进行建模。具体而言，Ubert从各个方面编码先验知识，统一地构建了多个NLU任务的学习表示，这有利于增强捕获共同语义理解的能力。通过使用Biaffine对原始文本的开始和终端位置进行模型对成对，可以将各种分类和提取结构转换为通用的跨度编码方法。实验表明，Ubert赢得了2022年AIWIN的首个价格 - 世界人工智能创新竞赛，中国保险很少付出的多任务轨道，并意识到了广泛的信息提取和语言推理任务的统一。
Even as pre-trained language models share a semantic encoder, natural language understanding suffers from a diversity of output schemas. In this paper, we propose UBERT, a unified bidirectional language understanding model based on BERT framework, which can universally model the training objects of different NLU tasks through a biaffine network. Specifically, UBERT encodes prior knowledge from various aspects, uniformly constructing learning representations across multiple NLU tasks, which is conducive to enhancing the ability to capture common semantic understanding. By using the biaffine to model scores pair of the start and end position of the original text, various classification and extraction structures can be converted into a universal, span-decoding approach. Experiments show that UBERT wins the first price in the 2022 AIWIN - World Artificial Intelligence Innovation Competition, Chinese insurance few-shot multi-task track, and realizes the unification of extensive information extraction and linguistic reasoning tasks.
