Answering Event-Related Questions over Long-Term News Article Archives [chapter]

Jiexin Wang, Adam Jatowt, Michael Färber, Masatoshi Yoshikawa
2020 Lecture Notes in Computer Science  
Long-term news article archives are valuable resources about our past, allowing people to know detailed information of events that occurred at specific time points. To make better use of such heritage collections, this work considers the task of large scale question answering on long-term news article archives. Questions on such archives are often eventrelated. In addition, they usually exhibit strong temporal aspects and can be roughly categorized into two types: (1) ones containing explicit
more » ... mporal expressions, and (2) ones only implicitly associated with particular time periods. We focus on the latter type as such questions are more difficult to be answered, and we propose a retriever-reader model with an additional module for reranking articles by exploiting temporal information from different angles. Experimental results on carefully constructed test set show that our model outperforms the existing question answering systems, thanks to an additional module that finds more relevant documents.
doi:10.1007/978-3-030-45439-5_51 fatcat:i4t6zxhfyffflmcimch3ro7zcq