Semantic parsing is a process to obtain structure representations. In this work, we propose a deep-learning model that competed at DatsSet MSParS "a Multi-perspective Semantic ParSing Dataset for Knowledge-based Question Answering". We used Sequence to Sequence(Seq2Seq), on encoder end using a pre-trained word embeddings on a big collection of Wikipedia 2014 + Gigaword 5 of glove.6B.100d.  We achieve a 65.63\% BLEU score on dev,  accuracy is 84.7\%.