Table Search Using a Deep Contextualized Language Model
Table 6. Results on WebQueryTable dataset.
['Method Name', 'MAP']
Though we conclude that the max salience selector with row items is the best method, the conclusion may depend on the corpus. Therefore, we also conduct experiments on the dataset from another domain. (sun2019content). Unlike WikiTables where all the tables are from Wikipedia, the tables in WebQueryTable are collected from queried web pages returned by a commercial search engine. In total, 21,113 query-table pairs are manually annotated and the dataset is pre-split into training (70%), development (10%) and test (20%). In this scenario, no additional features are available for this corpus so only BERT features are used. Additionally, table caption, sub-caption and headers are used as context fields. The preprocessing is the same with WikiTables. We do not use the development set since we do not search for hyperparameters. We calculate the MAP scores of our models which are also reported by Sun et al. (sun2019content). S5SS4—that max salience selector with row items is the best strategy. Therefore, we can see that training BERT on row items with max salience selector is also an effective strategy for datasets in other domains, which makes the answers to RQ2 and RQ3 more convincing.