Abstract
Semantic parsing is a challenging and important
task which aims to convert a natural language sentence to a logical form. Existing neural semantic parsing methods mainly use (Q-L) pairs to train a sequence-to-sequence
model. However, the amount of existing Q-L labeled data is limited and hard to obtain. We propose
an effective method which substantially utilizes labeling information from other tasks to enhance the
training of a semantic parser. We design a multitask learning model to train question type classifi-
cation, entity mention detection together with question semantic parsing using a shared encoder. We
propose a weakly supervised learning method to
enhance our multi-task learning model with paraphrase data, based on the idea that the paraphrased
questions should have the same logical form and
question type information. Finally, we integrate the
weakly supervised multi-task learning method to
an encoder-decoder framework. Experiments on a
newly constructed dataset and ComplexWebQuestions show that our proposed method outperforms
state-of-the-art methods which demonstrates the effectiveness and robustness of our method