| Challenge: | Data preparation is one of the most expensive and time-consuming steps when performing analytics or building machine learning models. |
| Approach: | They propose a retrieval augmented transformer model that is self-trained for table augmentation tasks. |
| Outcome: | The proposed model outperforms current state-of-the-art models on EntiTables and WebTables. |
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| Challenge: | Tabular datasets in industrial settings often encompass extensive data with numerous rows and columns. |
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| Challenge: | Existing tables models require linearization of the table structure, where row or column order is encoded as an unwanted bias. |
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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. |
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| Challenge: | a recent study compares transformer-based models with a greener and more sustainable alternative. |
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Michał Pietruszka, Michał Turski, Łukasz Borchmann, Tomasz Dwojak, Gabriela Nowakowska, Karolina Szyndler, Dawid Jurkiewicz, Łukasz Garncarek
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Named Entity Recognition in Industrial Tables using Tabular Language Models (2022.emnlp-industry)
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Kun Zhang, Xiexiong Lin, Yuanzhuo Wang, Xin Zhang, Fei Sun, Cen Jianhe, Hexiang Tan, Xuhui Jiang, Huawei Shen
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Capturing Row and Column Semantics in Transformer Based Question Answering over Tables (2021.naacl-main)
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Michael Glass, Mustafa Canim, Alfio Gliozzo, Saneem Chemmengath, Vishwajeet Kumar, Rishav Chakravarti, Avi Sil, Feifei Pan, Samarth Bharadwaj, Nicolas Rodolfo Fauceglia
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Generative Table Pre-training Empowers Models for Tabular Prediction (2023.emnlp-main)
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| Challenge: | Existing methods to use table pre-training to boost tabular prediction performance remain open . a bachelor's degree earns less than 50K, and a generative LM can be used to unify tasks via one LM. |
| Approach: | They propose a method that leverages table pre-training to empower tabular prediction models. |
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