Time is one of the crucial factors in real-world question answering (QA)
problems. However, language models have difficulty understanding the
relationships between time specifiers, such as 'after' and 'before', and
numbers, since existing QA datasets do not include sufficient time expressions.
To address this issue, we propose a Time-Context aware Question Answering
(TCQA) framework. We suggest a Time-Context dependent Span Extraction (TCSE)
task, and build a time-context dependent data generation framework for model
training. Moreover, we present a metric to evaluate the time awareness of the
QA model using TCSE. The TCSE task consists of a question and four sentence
candidates classified as correct or incorrect based on time and context. The
model is trained to extract the answer span from the sentence that is both
correct in time and context. The model trained with TCQA outperforms baseline
models up to 8.5 of the F1-score in the TimeQA dataset. Our dataset and code
are available at this https URL

提出了一个新的时间上下文感知问答（TCQA）框架，通过时间上下文依赖的数据生成框架训练模型，提供了一个用于评估 QA 模型时间感知能力的度量标准，通过数据集和代码验证，TCQA 模型在 TimeQA 数据集上的 F1 分数优于基线模型至少 8.5。