| 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. |
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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 . |
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Knowledge-Aware Reasoning over Multimodal Semi-structured Tables (2024.findings-emnlp)
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Suyash Mathur, Jainit Bafna, Kunal Kartik, Harshita Khandelwal, Manish Shrivastava, Vivek Gupta, Mohit Bansal, Dan Roth
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Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web (2020.acl-tutorials)
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| Challenge: | a tutorial explores the commonalities in the challenges and solutions developed to address information extraction from the World Wide Web. |
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TempTabQA: Temporal Question Answering for Semi-Structured Tables (2023.emnlp-main)
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| Challenge: | Semi-structured data often include temporal information about entities, either implicitly or explicitly. |
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Logical Inference for Counting on Semi-structured Tables (2022.acl-srw)
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| Challenge: | Natural Language Inference (NLI) tasks require numerical understanding to perform a numerical type of inference, such as counting. |
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Text-to-Table: A New Way of Information Extraction (2022.acl-long)
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| Challenge: | Existing methods for information extraction are not well understood . text-to-table is a problem that aims to extract information from text data . |
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| Challenge: | Multi-hop inference suffers from semantic drift, or the tendency for chains of reasoning to "drift"' to unrelated topics. |
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Representations for Question Answering from Documents with Tables and Text (2021.eacl-main)
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| Challenge: | a study aims to improve question answering on tables by refining table representations based on textual context. |
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Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
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DuoRAT: Towards Simpler Text-to-SQL Models (2021.naacl-main)
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| Challenge: | Recent text-to-SQL models can translate natural language questions to corresponding SQL queries on unseen databases. |
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