Question Answering in Natural Language: the Special Case of Temporal
  Expressions
Armand Stricker
Although general question answering has been well explored in recent years,
temporal question answering is a task which has not received as much focus. Our
work aims to leverage a popular approach used for general question answering,
answer extraction, in order to find answers to temporal questions within a
paragraph. To train our model, we propose a new dataset, inspired by SQuAD,
specifically tailored to provide rich temporal information. We chose to adapt
the corpus WikiWars, which contains several documents on history's greatest
conflicts. Our evaluation shows that a deep learning model trained to perform
pattern matching, often used in general question answering, can be adapted to
temporal question answering, if we accept to ask questions whose answers must
be directly present within a text.