论文标题
通用自然语言处理有限的注释：尝试几乎
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start
论文作者
论文摘要
解决不同NLP问题的一种标准方法是首先构建特定于问题的数据集，然后构建一个模型以适合该数据集。为了构建最终的人工智能，我们希望一台可以处理各种新问题的机器，而特定于任务的注释有限。我们将文字构成作为此类NLP问题的统一求解器。但是，当前对文本元素的研究并未在以下问题上散发出太多墨水：（i）鉴定的文本构成系统在跨域中仅使用少数特定领域的示例概括了如何概括？ （ii）什么时候值得将NLP任务转换为文本需要？我们认为，如果我们能为此任务获得丰富的注释，那么转换是不必要的。文本需要真正重要，特别是在目标NLP任务没有足够的注释时。 通用NLP可能可以通过不同的例程来实现。在这项工作中，我们介绍了通用的少数文本元素（UFO-Entail）。我们证明，该框架使一个预处理的元素模型能够在几次设置的新元素域上很好地运行，并显示出在终点NLP任务的统一求解器中，当端任任务的注释有限时，诸如问答答案和核心解决方案之类的统一求解器。代码：https：//github.com/salesforce/universalfewshotnlp
A standard way to address different NLP problems is by first constructing a problem-specific dataset, then building a model to fit this dataset. To build the ultimate artificial intelligence, we desire a single machine that can handle diverse new problems, for which task-specific annotations are limited. We bring up textual entailment as a unified solver for such NLP problems. However, current research of textual entailment has not spilled much ink on the following questions: (i) How well does a pretrained textual entailment system generalize across domains with only a handful of domain-specific examples? and (ii) When is it worth transforming an NLP task into textual entailment? We argue that the transforming is unnecessary if we can obtain rich annotations for this task. Textual entailment really matters particularly when the target NLP task has insufficient annotations. Universal NLP can be probably achieved through different routines. In this work, we introduce Universal Few-shot textual Entailment (UFO-Entail). We demonstrate that this framework enables a pretrained entailment model to work well on new entailment domains in a few-shot setting, and show its effectiveness as a unified solver for several downstream NLP tasks such as question answering and coreference resolution when the end-task annotations are limited. Code: https://github.com/salesforce/UniversalFewShotNLP
