  Natural interface to database (NLIDB) has been researched a lot during the
past decades. In the core of NLIDB, is a semantic parser used to convert
natural language into SQL. Solutions from traditional NLP methodology focuses
on grammar rule pattern learning and pairing via intermediate logic forms.
Although those methods give an acceptable performance on certain specific
database and parsing tasks, they are hard to generalize and scale. On the other
hand, recent progress in neural deep learning seems to provide a promising
direction towards building a general NLIDB system. Unlike the traditional
approach, those neural methodologies treat the parsing problem as a
sequence-to-sequence learning problem. In this paper, we experimented on
several sequence-to-sequence learning models and evaluate their performance on
general database parsing task.
