KB-QA based on multi-task learning and negative sample generation

Cheng, Liao; Xie, Feiyang; Ren, Jiangtao

doi:10.1016/j.ins.2021.06.021

Cited by 3 publications

(1 citation statement)

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“…For example, Yang et al [ 33 ] proposed a new multi-tasking and knowledge-enhanced multi-head interactive attention network, which classifies questions as auxiliary tasks and conducts community question answering through multi-tasking learning. In addition, multi-tasking joint learning can enhance the generalization ability of the model [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints

Yang

et al. 2022

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In the process of bridge management, large amounts of domain information are accumulated, such as basic attributes, structural defects, technical conditions, etc. However, the valuable information is not fully utilized, resulting in insufficient knowledge service in the field of bridge management. To tackle these problems, this paper proposes a complex knowledge base question answering (C-KBQA) framework for intelligent bridge management based on multi-task learning (MTL) and cross-task constraints (CTC). First, with C-KBQA as the main task, part-of-speech (POS) tagging, topic entity extraction (TEE), and question classification (QC) as auxiliary tasks, an MTL framework is built by sharing encoders and parameters, thereby effectively avoiding the error propagation problem of the pipeline model. Second, cross-task semantic constraints are provided for different subtasks via POS embeddings, entity embeddings, and question-type embeddings. Finally, using template matching, relevant query statements are generated and interaction with the knowledge base is established. The experimental results show that the proposed model outperforms compared mainstream models in terms of TEE and QC on bridge management datasets, and its performance in C-KBQA is outstanding.

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Section: Related Workmentioning

confidence: 99%