Papers with tabular inference

3 papers
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.
Incorporating External Knowledge to Enhance Tabular Reasoning (2021.naacl-main)

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Challenge: Existing models struggle with tabular inference due to contextualized embeddings of text.
Approach: They propose easy and effective modifications to how information is presented to a model for tabular inference.
Outcome: The proposed modifications significantly improve tabular inference performance on large datasets.
Realistic Data Augmentation Framework for Enhancing Tabular Reasoning (2022.findings-emnlp)

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Challenge: Existing approaches to constructing training data for Natural Language Inference (NLI) tasks are expensive and time consuming.
Approach: They propose a semi-automated framework for data augmentation for tabular inference . framework generates hypothesis templates transferable to similar tables . authors say framework could generate human-like tabular examples .
Outcome: The proposed framework generates human-like tabular inference examples . it is based on human-written constraints and premise paraphrasing .

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