While transformers demonstrate impressive performance on many knowledge intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs) remains limited, as shown on several slot-filling, question-answering (QA), fact verification, and entity-linking tasks. In this paper, we implement an efficient, data-programming technique that enriches training data with KB-derived context and improves transformer utilization of encoded knowledge when fine-tuning for a particular QA task, namely answer sentence selection (AS2). Our method outperforms state of the art transformer approach on WikiQA and TrecQA, two widely studied AS2 benchmarks, increasing by 2.0% p@1, 1.3% MAP, 1.1% MRR, and 4.4% p@1, 0.9% MAP, 2.4% MRR, respectively. To demonstrate our improvements in an industry setting, we additionally evaluate our approach on a proprietary dataset of Alexa QA pairs, and show increase of 2.3% F1 and 2.0% MAP. We additionally find that these improvements remain even when KB context is omitted at inference time, allowing for the use of our models within existing transformer workflows without additional latency or deployment costs.

本文利用数据编程技术设计了一种有效方法，可以使用知识库获得的上下文信息来进行transformers在回答句子选择QA任务中的精细调整，模型优化效果在WikiQA和TrecQA基准测试中比SOTA transformer方法提高了2.0％ p@1、1.3％ MAP、1.1％ MRR、4.4％ p@1、0.9％ MAP、2.4％ MRR，同时添加了自己的Alexa QA数据集。该方法能够使得模型在已有transformer工作流程上增加使用知识库上下文后的改进结果，而无需附加任何的延迟或实施成本

使用知识库的数据编程提高变压器微调答案句子选取