A Dataset for Answering Time-Sensitive Questions (2108.06314v5)

Abstract: Time is an important dimension in our physical world. Lots of facts can evolve with respect to time. For example, the U.S. President might change every four years. Therefore, it is important to consider the time dimension and empower the existing QA models to reason over time. However, the existing QA datasets contain rather few time-sensitive questions, hence not suitable for diagnosing or benchmarking the model's temporal reasoning capability. In order to promote research in this direction, we propose to construct a time-sensitive QA dataset. The dataset is constructed by 1) mining time-evolving facts from WikiData and aligning them to their corresponding Wikipedia page, 2) employing crowd workers to verify and calibrate these noisy facts, 3) generating question-answer pairs based on the annotated time-sensitive facts. Our dataset poses challenges in the aspect of both temporal understanding and temporal reasoning. We evaluate different SoTA long-document QA systems like BigBird and FiD on our dataset. The best-performing model FiD can only achieve 46\% accuracy, still far behind the human performance of 87\%. We demonstrate that these models are still lacking the ability to perform consistent temporal reasoning. Therefore, we believe that our dataset could serve as a benchmark to develop NLP models more sensitive to temporal shifts. The dataset and code are released in~\url{https://github.com/wenhuchen/Time-Sensitive-QA}.

Overview of "A Dataset for Answering Time-Sensitive Questions"

The research paper titled "A Dataset for Answering Time-Sensitive Questions" addresses an important gap in the field of NLP regarding the capability of Question Answering (QA) systems to handle time-sensitive queries. The authors have created a dataset specifically designed to evaluate and enhance the temporal reasoning abilities of current QA models. This work is fundamental as it recognizes the evolving nature of factual information, which can vary over time.

Dataset Construction

The dataset, named TimeQA, is meticulously constructed to emphasize temporal reasoning challenges. The process involves multiple steps:

1. Fact Extraction: The authors begin by mining time-evolving facts from WikiData, which are then aligned with corresponding entries on Wikipedia. This ensures that the dataset is grounded in publicly accessible, real-world information.
2. Human Verification: Given the inherent noise in automated data extraction, crowd workers are employed to verify and calibrate the temporal facts, improving the dataset's reliability.
3. Question-Answer Pair Generation: Finally, the authors generate question-answer pairs based on verified facts using various templates that incorporate temporal reasoning challenges. This results in a dataset split into 'easy' and 'hard' versions, designed to assess different levels of temporal reasoning capabilities.

The dataset includes a diverse range of questions that require understanding both explicit and implicit temporal information, demanding models to recognize and reason through temporal relationships such as "before", "after", and "between".

Evaluation and Findings

The paper evaluates several state-of-the-art (SoTA) QA systems, such as BigBird and FiD, using the TimeQA dataset. The results are quite revealing:

• The highest-performing model, FiD, only achieved 46% accuracy on the hard version of the dataset. This is significantly below human performance, which is around 87%. This substantial gap suggests current models struggle with temporal reasoning tasks.
• The accuracy drop from the easy to hard mode is stark, indicating that implicit temporal information and complex temporal reasoning pose significant challenges to current QA models.

Implications and Future Directions

The paper implies that existing QA models' performance is considerably hindered by their inability to effectively process and reason with temporal information. As such, TimeQA not only functions as a rigorous benchmark but also as a catalyst that could inspire the development of temporal-aware NLP models.

From a theoretical standpoint, the research underscores the necessity of integrating advanced temporal reasoning mechanisms into NLP models, which could be future extensions to enhance understanding of events over time. Practically, improving such models would have far-reaching implications for various applications, including digital assistants, automated content generation, and real-time information retrieval systems.

Conclusion

In conclusion, this paper makes a notable contribution by providing a novel benchmark for time-sensitive QA systems. By highlighting the inadequacies of current models in temporal reasoning, it opens avenues for developing more sophisticated and temporally-aware NLP systems. As the temporal aspect is intrinsic to understanding real-world scenarios, advancements in this area are likely to play a pivotal role in the next generation of language technologies.