{'fraction_non_alphanumeric': 0.022727272727272728, 'fraction_numerical': 0.000946969696969697, 'mean_word_length': 5.109826589595376, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
F-SQL: Fuse Table Schema and Table Content for Single-Table Text2SQL Generation

Automatically parsing SQL queries from natural languages can help non-professionals access databases and improve the efficiency of information utilization. It is a long-term research issue and has recently received attention from the relevant communities. Although previous researches have provided some workable solutions, most of them only consider table schemas and natural language questions when parsing SQL queries, and do not use table contents. We observe that table contents can provide more helpful information for some user questions. In this paper, we propose a novel neural network approach, F-SQL, to focus on solving the problem of table content utilization. In particular, we employ the gate mechanism to fuse table schemas and table contents and get the more different representation about table schemas. We test this idea on the WikiSQL and TableQA datasets. Experimental results show that F-SQL achieves new state-of-the-art results on WikiSQL and TableQA.