TempTabQA: Temporal Question Answering for Semi-Structured Tables
This addresses the problem of temporal reasoning in NLP for semi-structured data, providing a challenging benchmark, though it is incremental as it builds on existing QA tasks.
The authors introduced the task of temporal question answering on semi-structured tables and created the TempTabQA dataset with 11,454 question-answer pairs from Wikipedia Infobox tables, finding that top-performing LLMs lag behind human performance by over 13.5 F1 points.
Semi-structured data, such as Infobox tables, often include temporal information about entities, either implicitly or explicitly. Can current NLP systems reason about such information in semi-structured tables? To tackle this question, we introduce the task of temporal question answering on semi-structured tables. We present a dataset, TempTabQA, which comprises 11,454 question-answer pairs extracted from 1,208 Wikipedia Infobox tables spanning more than 90 distinct domains. Using this dataset, we evaluate several state-of-the-art models for temporal reasoning. We observe that even the top-performing LLMs lag behind human performance by more than 13.5 F1 points. Given these results, our dataset has the potential to serve as a challenging benchmark to improve the temporal reasoning capabilities of NLP models.