One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the
structural property of the SQL queries, the seq2seq model takes the responsibility of parsing …

Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-
based natural language (NL) understanding tasks. Such models are typically trained on free …

When translating natural language questions into SQL queries to answer questions from a
database, contemporary semantic parsing models struggle to generalize to unseen …

Answering complex factual questions has drawn a lot of attention. Researchers leverage
various data sources to support complex QA, such as unstructured texts, structured …

We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.
IRNet aims to address two challenges: 1) the mismatch between intents expressed in natural …

Recent progress in language model pre-training has achieved a great success via
leveraging large-scale unstructured textual data. However, it is still a challenge to apply pre …

Abstract Table Question Answering (Table QA) refers to providing precise answers from
tables to answer a user's question. In recent years, there have been a lot of works on table …

Answering complex natural language questions on knowledge graphs (KGQA) is a
challenging task. It requires reasoning with the input natural language questions as well as …

Natural language interfaces (NLls) have shown great promise for visual data analysis,
allowing people to flexibly specify and interact with visualizations. However, developing …

Addressing the mismatch between natural language descriptions and the corresponding
SQL queries is a key challenge for text-to-SQL translation. To bridge this gap, we propose …