{"type":"ScholarlyArticle","title":"Recurrent Neural Network for Text Classification with Multi-Task Learning","authors":[{"name":"Pengfei Liu","slug":"pengfei-liu","given":"Pengfei","family":"Liu","affiliation":""},{"name":"Xipeng Qiu","slug":"xipeng-qiu","given":"Xipeng","family":"Qiu","affiliation":""},{"name":"Xuanjing Huang","slug":"xuanjing-huang","given":"Xuanjing","family":"Huang","affiliation":""}],"abstract":"Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multi-task learning framework to jointly learn across multiple related tasks. Based on recurrent neural network, we propose three different mechanisms of sharing information to model text with task-specific and shared layers. The entire network is trained jointly on all these tasks. Experiments on four benchmark text classification tasks show that our proposed models can improve the performance of a task with the help of other related tasks.","doi":null,"url":"https://ijcai.org/proceedings/2016/408","pdf":"https://ijcai.org/proceedings/2016/0408.pdf","markdown":"https://ijcai.org/proceedings/2016/408.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/408","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":408,"pages":{"first":2873,"last":2878},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}