Improving question answering for event-focused questions in temporal collections of news articles

Temporal collections of news articles (or news archives) contain numerous accurate and time-aligned articles, which offer immense value to our society, helping users to know details of events that occurred at specific time points in the past. Currently, the access to such collections is rather difficult for average users due to their large sizes and complexities. For better use of these valuable resources on our heritage, this study considers the task of machine reading at scale on long-term news article archives. We make use of the observation that questions on news archives are usually related to particular events and show strong temporal aspects. We propose a large scale question answering model designed specifically for long-term news article collections, with an additional module for re-ranking articles by using temporal information from different perspectives. The experimental results show that our model is superior to the existing question answering systems, thanks to dedicated module that allows finding more relevant documents.