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While diverse question answering (QA) datasets have been proposed andcontributed significantly to the development of deep learning models for QAtasks, the existing datasets fall short in two aspects. First, we lack QAdatasets covering complex questions that involve answers as well as thereasoning processes to get the answers. As a result, the state-of-the-art QAresearch on numerical reasoning still focuses on simple calculations and doesnot provide the mathematical expressions or evidences justifying the answers.Second, the QA community has contributed much effort to improving theinterpretability of QA models. However, these models fail to explicitly showthe reasoning process, such as the evidence order for reasoning and theinteractions between different pieces of evidence. To address the aboveshortcomings, we introduce NOAHQA, a conversational and bilingual QA datasetwith questions requiring numerical reasoning with compound mathematicalexpressions. With NOAHQA, we develop an interpretable reasoning graph as wellas the appropriate evaluation metric to measure the answer quality. We evaluatethe state-of-the-art QA models trained using existing QA datasets on NOAHQA andshow that the best among them can only achieve 55.5 exact match scores, whilethe human performance is 89.7. We also present a new QA model for generating areasoning graph where the reasoning graph metric still has a large gap comparedwith that of humans, e.g., 28 scores.

NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset