Multi-Instance Learning for End-to-End Knowledge Base Question Answering
arXiv:1903.02652
Abstract
End-to-end training has been a popular approach for knowledge base question answering (KBQA). However, real world applications often contain answers of varied quality for users' questions. It is not appropriate to treat all available answers of a user question equally. This paper proposes a novel approach based on multiple instance learning to address the problem of noisy answers by exploring consensus among answers to the same question in training end-to-end KBQA models. In particular, the QA pairs are organized into bags with dynamic instance selection and different options of instance weighting. Curriculum learning is utilized to select instance bags during training. On the public CQA dataset, the new method significantly improves both entity accuracy and the Rouge-L score over a state-of-the-art end-to-end KBQA baseline.
References in corpus (6)
- Adam: A Method for Stochastic Optimization
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
- QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
- The Natural Language Decathlon: Multitask Learning as Question Answering
- Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks