Faithful Temporal Question Answering over Heterogeneous Sources
Zhen Jia Philipp Christmann Gerhard Weikum
Temporal question answering (QA) involves time constraints, with phrases such
as "... in 2019" or "... before COVID". In the former, time is an explicit
condition, in the latter it is implicit. State-of-the-art methods have
limitations along three dimensions. First, with neural inference, time
constraints are merely soft-matched, giving room to invalid or inexplicable
answers. Second, questions with implicit time are poorly supported. Third,
answers come from a single source: either a knowledge base (KB) or a text
corpus. We propose a temporal QA system that addresses these shortcomings.
First, it enforces temporal constraints for faithful answering with tangible
evidence. Second, it properly handles implicit questions. Third, it operates
over heterogeneous sources, covering KB, text and web tables in a unified
manner. The method has three stages: (i) understanding the question and its
temporal conditions, (ii) retrieving evidence from all sources, and (iii)
faithfully answering the question. As implicit questions are sparse in prior
benchmarks, we introduce a principled method for generating diverse questions.
Experiments show superior performance over a suite of baselines.