论文标题
上下文生成改善了开放域问题回答
Context Generation Improves Open Domain Question Answering
论文作者
论文摘要
封闭式书本答案（QA）要求模型直接回答一个开放域问题，而无需访问任何外部知识。封闭式质量保证的事先工作要么直接填充或提示验证的语言模型（LM）来利用存储的知识。但是，他们没有完全利用参数化的知识。为了解决这个问题，我们提出了一个两阶段的封闭式质量检查框架，该框架采用粗略的方法来提取相关知识并回答问题。我们的方法首先通过提示预贴LM来为给定问题产生相关上下文。然后，我们提示相同的LM使用生成的上下文和问题进行回答预测。此外，为了消除由上下文不确定性引起的故障，我们在生成的上下文上边缘化。三个质量检查基准的实验结果表明，我们的方法显着优于先前的闭合书质量检查方法（例如，完全匹配68.6％vs. 55.3％），并且与开放式方法相当，可以利用外部知识来源（例如68.6％vs. 68.0％）。我们的方法能够更好地利用预验证的LMS中的存储知识，而无需添加额外的可学习参数或需要填充，并为将预处理的LMS与外部知识集成的混合模型铺平了道路。
Closed-book question answering (QA) requires a model to directly answer an open-domain question without access to any external knowledge. Prior work on closed-book QA either directly finetunes or prompts a pretrained language model (LM) to leverage the stored knowledge. However, they do not fully exploit the parameterized knowledge. To address this issue, we propose a two-stage, closed-book QA framework which employs a coarse-to-fine approach to extract relevant knowledge and answer a question. Our approach first generates a related context for a given question by prompting a pretrained LM. We then prompt the same LM for answer prediction using the generated context and the question. Additionally, to eliminate failure caused by context uncertainty, we marginalize over generated contexts. Experimental results on three QA benchmarks show that our method significantly outperforms previous closed-book QA methods (e.g. exact matching 68.6% vs. 55.3%), and is on par with open-book methods that exploit external knowledge sources (e.g. 68.6% vs. 68.0%). Our method is able to better exploit the stored knowledge in pretrained LMs without adding extra learnable parameters or needing finetuning, and paves the way for hybrid models that integrate pretrained LMs with external knowledge.
