Abstract
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, a state-of-the-art question answering corpus, specifically tailored to provide rich temporal information by adapting the corpus WikiWars, which contains several documents on history’s greatest conflicts. Our evaluation shows that a pattern matching deep learning model, 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.- Anthology ID:
- 2021.ranlp-srw.26
- Volume:
- Proceedings of the Student Research Workshop Associated with RANLP 2021
- Month:
- September
- Year:
- 2021
- Address:
- Online
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 184–192
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-srw.26
- DOI:
- Cite (ACL):
- Armand Stricker. 2021. Question answering in Natural Language: the Special Case of Temporal Expressions. In Proceedings of the Student Research Workshop Associated with RANLP 2021, pages 184–192, Online. INCOMA Ltd..
- Cite (Informal):
- Question answering in Natural Language: the Special Case of Temporal Expressions (Stricker, RANLP 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.ranlp-srw.26.pdf
- Data
- SQuAD