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NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset ...
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Abstract:
While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall short in two aspects. First, we lack QA datasets covering complex questions that involve answers as well as the reasoning processes to get them. As a result, the state-of-the-art QA research on numerical reasoning still focuses on simple calculations and does not provide the mathematical expressions or evidence justifying the answers. Second, the QA community has contributed a lot of effort to improve the interpretability of QA models. However, they fail to explicitly show the reasoning process, such as the evidence order for reasoning and the interactions between different pieces of evidence. To address the above shortcoming, we introduce NOAHQA, a conversational and bilingual QA dataset with questions requiring numerical reasoning with compound mathematical expressions. With NOAHQA, we develop an interpretable reasoning graph as ...
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URL: https://underline.io/lecture/38510-noahqa-numerical-reasoning-with-interpretable-graph-question-answering-dataset https://dx.doi.org/10.48448/75ds-x611
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NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset ...
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Investigating Math Word Problems using Pretrained Multilingual Language Models ...
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Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering ...
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Unsupervised Deep Structured Semantic Models for Commonsense Reasoning ...
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Do we really need fully unsupervised cross-lingual embeddings?
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Racial mimesis: Translation, literature, and self -fashioning in modern China.
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An Empirical Study of Tokenization Strategies for Biomedical Information Retrieval
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