  Tables are an important form of structured data for both human and machine
readers alike, providing answers to questions that cannot, or cannot easily, be
found in texts. Recent work has designed special models and training paradigms
for table-related tasks such as table-based question answering and table
retrieval. Though effective, they add complexity in both modeling and data
acquisition compared to generic text solutions and obscure which elements are
truly beneficial. In this work, we focus on the task of table retrieval, and
ask: "is table-specific model design necessary for table retrieval, or can a
simpler text-based model be effectively used to achieve a similar result?"
First, we perform an analysis on a table-based portion of the Natural Questions
dataset (NQ-table), and find that structure plays a negligible role in more
than 70% of the cases. Based on this, we experiment with a general Dense
Passage Retriever (DPR) based on text and a specialized Dense Table Retriever
(DTR) that uses table-specific model designs. We find that DPR performs well
without any table-specific design and training, and even achieves superior
results compared to DTR when fine-tuned on properly linearized tables. We then
experiment with three modules to explicitly encode table structures, namely
auxiliary row/column embeddings, hard attention masks, and soft relation-based
attention biases. However, none of these yielded significant improvements,
suggesting that table-specific model design may not be necessary for table
retrieval.
