mlsanityData to 14 Oct 2026

Evaluating Answer Reranking Strategies in Time-sensitive Question Answering

arXiv 2503.04972 ↗First posted 6 Mar 2025cs.CL cs.IRcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 6 Mar 2025.

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9 of 16 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  4. 2021Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answeringcited by 178
  5. 2019End-to-End Open-Domain Question Answering with BERTserinicited by 152
  6. 2024MedExQA: Medical Question Answering Benchmark with Multiple Explanationscited by 60
  7. 2021ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collectionscited by 30
  8. 2025TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questionscited by 15
  9. 2020Predicting Event Time by Classifying Sub-Level Temporal Relations Induced from a Unified Representation of Time Anchorscited by 4

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Citing papers per year: 2025: 1; 2026: 1.

2 papers.