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Tables are an important form of structured data for both human and machinereaders alike, providing answers to questions that cannot, or cannot easily, befound in texts. Recent work has designed special models and training paradigmsfor table-related tasks such as table-based question answering and tableretrieval. Though effective, they add complexity in both modeling and dataacquisition compared to generic text solutions and obscure which elements aretruly beneficial. In this work, we focus on the task of table retrieval, andask: "is table-specific model design necessary for table retrieval, or can asimpler text-based model be effectively used to achieve a similar result?"First, we perform an analysis on a table-based portion of the Natural Questionsdataset (NQ-table), and find that structure plays a negligible role in morethan 70% of the cases. Based on this, we experiment with a general DensePassage Retriever (DPR) based on text and a specialized Dense Table Retriever(DTR) that uses table-specific model designs. We find that DPR performs wellwithout any table-specific design and training, and even achieves superiorresults compared to DTR when fine-tuned on properly linearized tables. We thenexperiment with three modules to explicitly encode table structures, namelyauxiliary row/column embeddings, hard attention masks, and soft relation-basedattention biases. However, none of these yielded significant improvements,suggesting that table-specific model design may not be necessary for tableretrieval.

Table Retrieval May Not Necessitate Table-specific Model Design