Text2Insight combines an LLM text-to-SQL step with a rule-based chart predictor and BERT-based question answering and prediction, but its end-to-end performance claims rest on circular or missing evaluation.
TSQA: Tabular Scenario Based Question Answering
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abstract
Scenario-based question answering (SQA) has attracted an increasing research interest. Compared with the well-studied machine reading comprehension (MRC), SQA is a more challenging task: a scenario may contain not only a textual passage to read but also structured data like tables, i.e., tabular scenario based question answering (TSQA). AI applications of TSQA such as answering multiple-choice questions in high-school exams require synthesizing data in multiple cells and combining tables with texts and domain knowledge to infer answers. To support the study of this task, we construct GeoTSQA. This dataset contains 1k real questions contextualized by tabular scenarios in the geography domain. To solve the task, we extend state-of-the-art MRC methods with TTGen, a novel table-to-text generator. It generates sentences from variously synthesized tabular data and feeds the downstream MRC method with the most useful sentences. Its sentence ranking model fuses the information in the scenario, question, and domain knowledge. Our approach outperforms a variety of strong baseline methods on GeoTSQA.
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cs.AI 1years
2024 1verdicts
REJECT 1representative citing papers
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Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture
Text2Insight combines an LLM text-to-SQL step with a rule-based chart predictor and BERT-based question answering and prediction, but its end-to-end performance claims rest on circular or missing evaluation.