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• W3035275890 abstract "In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between them. We argue that such data can prove as a testing ground for understanding how we reason about information. To study this, we introduce a new dataset called INFOTABS, comprising of human-written textual hypotheses based on premises that are tables extracted from Wikipedia info-boxes. Our analysis shows that the semi-structured, multi-domain and heterogeneous nature of the premises admits complex, multi-faceted reasoning. Experiments reveal that, while human annotators agree on the relationships between a table-hypothesis pair, several standard modeling strategies are unsuccessful at the task, suggesting that reasoning about tables can pose a difficult modeling challenge." @default.
• W3035275890 created "2020-06-19" @default.
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• W3035275890 date "2020-01-01" @default.
• W3035275890 modified "2023-10-14" @default.
• W3035275890 title "INFOTABS: Inference on Tables as Semi-structured Data" @default.
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• W3035275890 doi "https://doi.org/10.18653/v1/2020.acl-main.210" @default.
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