Weakly Supervised Multi-task Learning for Semantic Parsing

Weakly Supervised Multi-task Learning for Semantic Parsing

Bo Shao, Yeyun Gong, Junwei Bao, Jianshu Ji, Guihong Cao, Xiaola Lin, Nan Duan

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 3375-3381. https://doi.org/10.24963/ijcai.2019/468

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 multi-task learning model to train question type classification, 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.
Keywords:
Machine Learning: Transfer, Adaptation, Multi-task Learning
Natural Language Processing: Natural Language Semantics
Natural Language Processing: Tagging, chunking, and parsing