Papers with INFOTABS
Enhancing Tabular Reasoning with Pattern Exploiting Training (2022.aacl-main)
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| Challenge: | Existing methods based on pre-trained language models have shown superior performance over tabular tasks despite showing inherent problems such as not using the right evidence and inconsistent predictions across inputs. |
| Approach: | They utilize Pattern-Exploiting Training (PET) on pre-trained language models to strengthen tabular reasoning models’ pre-existing knowledge and reasoning abilities. |
| Outcome: | The proposed model exhibits superior understanding of knowledge facts and tabular reasoning compared to baseline models. |
INFOTABS: Inference on Tables as Semi-structured Data (2020.acl-main)
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| Challenge: | Existing models for text understanding lack human-parity across a wide array of reasoning skills. |
| Approach: | They propose an extension of the natural language inference task to include semi-structured tabulated text . they propose a semi-structural, multi-domain and heterogeneous nature of the premises that are tables extracted from Wikipedia info-boxes. |
| Outcome: | The proposed model outperforms baseline models on the GLUE benchmark suite. |