To enhance the proposed search strategies for detecting temporal ambiguity in open-domain question answering (ODQA), several improvements can be considered. Firstly, the integration of dynamic time frames that adapt based on the context of the question could be beneficial. Instead of relying on fixed time ranges, a model could analyze the content of the question to identify relevant historical or future periods, thereby allowing for a more nuanced understanding of temporal context.
Additionally, incorporating natural language processing techniques to extract temporal expressions from questions could improve the disambiguation process. For instance, using temporal tagging to identify phrases like "last year," "in the 1990s," or "next month" would allow the system to generate more contextually relevant disambiguation questions.
Moreover, expanding the dataset to include questions with varying granularity—such as months, days, or even events—could help the model learn to handle a broader spectrum of temporal contexts. Implementing machine learning techniques that leverage historical data trends and patterns could also enhance the model's ability to predict relevant time frames based on the nature of the question.
Finally, employing ensemble methods that combine multiple search strategies could lead to improved efficiency and accuracy in detecting temporal ambiguity, as different strategies may excel in different contexts.