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 https://github.com/sonjbin/TCQA

Last week, a report about sex robots warned about the'dark side' of the technology, which could involve issues of rape and paedophilia. And now The Salvation Army has had its say on the controversial sexbots. The charity claims that sex robots could'fuel demand for sex with people', and even lead traffickers to exploit more vulnerable individuals to meet this demand. The Salvation Army claims that sex robots could'fuel demand for sex with people', and even lead traffickers to exploit more vulnerable individuals to meet this demand And it indicates that sexbots could normalise a distorted power dynamic which devalues the other person involved when transferred to human interactions. This could encourage the objectification of women and children and a lack of respect for fellow human beings, according to the charity.