论文标题
开放域QA的两步问题检索
Two-Step Question Retrieval for Open-Domain QA
论文作者
论文摘要
猎犬阅读器管道在开放域质量检查中表现出了有希望的性能，但推理速度的遭受速度非常缓慢。最近提出的问题检索模型通过索引提问和搜索类似问题来解决此问题。这些模型显示出推理速度的显着提高，但与Retriever-Reader模型相比，质量检查率较低。本文提出了一个两步的问题检索模型，鱿鱼（顺序提出的密集检索）和远程培训的监督。鱿鱼使用两个双重编码器进行问题检索。第一步的检索器选择了TOP-K相似的问题，而第二步的检索器从Top-K问题中找到了最相似的问题。我们评估鱿鱼的性能和计算效率。结果表明，鱿鱼大大提高了现有问题检索模型的性能，而推理速度的损失却可以忽略不计。
The retriever-reader pipeline has shown promising performance in open-domain QA but suffers from a very slow inference speed. Recently proposed question retrieval models tackle this problem by indexing question-answer pairs and searching for similar questions. These models have shown a significant increase in inference speed, but at the cost of lower QA performance compared to the retriever-reader models. This paper proposes a two-step question retrieval model, SQuID (Sequential Question-Indexed Dense retrieval) and distant supervision for training. SQuID uses two bi-encoders for question retrieval. The first-step retriever selects top-k similar questions, and the second-step retriever finds the most similar question from the top-k questions. We evaluate the performance and the computational efficiency of SQuID. The results show that SQuID significantly increases the performance of existing question retrieval models with a negligible loss on inference speed.
