Lifelogs are descriptions of experiences that a person had during their life.
Lifelogs are created by fusing data from the multitude of digital services,
such as online photos, maps, shopping and content streaming services. Question
answering over lifelogs can offer personal assistants a critical resource when
they try to provide advice in context. However, obtaining answers to questions
over lifelogs is beyond the current state of the art of question answering
techniques for a variety of reasons, the most pronounced of which is that
lifelogs combine free text with some degree of structure such as temporal and
geographical information.
We create and publicly release TimelineQA1, a benchmark for accelerating
progress on querying lifelogs. TimelineQA generates lifelogs of imaginary
people. The episodes in the lifelog range from major life episodes such as high
school graduation to those that occur on a daily basis such as going for a run.
We describe a set of experiments on TimelineQA with several state-of-the-art QA
models. Our experiments reveal that for atomic queries, an extractive QA system
significantly out-performs a state-of-the-art retrieval-augmented QA system.
For multi-hop queries involving aggregates, we show that the best result is
obtained with a state-of-the-art table QA technique, assuming the ground truth
set of episodes for deriving the answer is available.

本文介绍了一个对 Lifelogs 进行问题回答的基准测试 TimelineQA1，并通过多个实验比较了几种 QA 模型的性能。实验结果表明，对于原子查询，抽取式 QA 系统明显优于一种最先进的检索增强 QA 系统。对于涉及聚合的多跳查询，我们展示了使用最先进的表格 QA 技术的最佳结果，前提是可用于推导答案的真实事件集合可用。