Evaluating Answer Reranking Strategies in Time-sensitive Question Answering
Kardan, Mehmet, Piryani, Bhawna, Jatowt, Adam
–arXiv.org Artificial Intelligence
Despite advancements in state-of-the-art models and information retrieval techniques, current systems still struggle to handle temporal information and to correctly answer detailed questions about past events. In this paper, we investigate the impact of temporal characteristics of answers in Question Answering (QA) by exploring several simple answer selection techniques. Our findings emphasize the role of temporal features in selecting the most relevant answers from diachronic document collections and highlight differences between explicit and implicit temporal questions.
arXiv.org Artificial Intelligence
Mar-6-2025
- Country:
- North America > United States
- Texas > Travis County
- Austin (0.04)
- New York > New York County
- New York City (0.04)
- Minnesota > Hennepin County
- Minneapolis (0.15)
- Texas > Travis County
- Europe > Austria
- Asia > Thailand
- North America > United States
- Genre:
- Research Report > New Finding (0.34)
- Technology: