Papers with TaBERT
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
| Challenge: | Existing transformer based approaches have been used to answer questions over tables. |
| Approach: | They propose a transformer based architecture that independently classifies rows and columns to identify relevant cells and a model that incorporates existing tables to improve efficiency. |
| Outcome: | The proposed model outperforms the state-of-the-art transformer based approaches on WikiSQL lookup questions and achieves 3.4% and 18.86% additional precision improvement on the standard WikisQL benchmark. |
A Relation Extraction Dataset for Knowledge Extraction from Web Tables (2022.coling-1)
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| Challenge: | Existing datasets with relational web-tables are either synthetic, or very small in size. |
| Approach: | They propose to annotate relational web-tables against a human-annotated dataset using crowd sourced annotators from MTurk. |
| Outcome: | The proposed dataset has 50x larger number of column pairs than the existing human-annotated benchmark. |
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data (2020.acl-main)
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| Challenge: | Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language understanding tasks. |
| Approach: | They propose a pretrained language model that jointly learns representations for NL sentences and (semi-)structured tables. |
| Outcome: | The proposed model performs best on the weakly-supervised semantic parsing benchmark WikiTableQuestions while performing competitively on the text-to-SQL dataset Spider. |