Before revision:  Our proposed model, SymTime, exploits distant supervision signals from the text itself and reasons over events' start time and duration to infer events' end time points. We show that our approach improves over baseline language models, gaining 5\% on the i.i.d. split and 9\% on an out-of-distribution test split .
After revision:  Our proposed model, SymTime, exploits distant supervision signals from large-scale text and uses temporal rules to combine start times and durations to infer end times. SYMTIME outperforms strong baseline systems on TRACIE by 5\% on the i.i.d. split and 9\% on an out-of-distribution test split .