Question Answering in Natural Language: the Special Case of Temporal Expressions

Understand

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.