Multi-hop Reasoning Research Articles

Convolutional End-to-End Memory Networks for Multi-Hop Reasoning

Machine reading and comprehension using differentiable reasoning models has recently been studied extensively, and memory networks have demonstrated promising performance on some reasoning tasks such as factual reasoning and basic deduction. However, as a natural language understanding model, memory networks still face challenges on the numeric representations for sentences, particularly the text representation method and the effectiveness of learned vector representations. In this paper, inspired by the convolution mechanism in the computer vision domain, a raw text representation architecture for question answering problem named convolutional end-to-end memory networks(CMemN2N) architecture is proposed. The convolutional architecture of the proposed model allows us to abstract the useful local information for reasoning to get the significant numeric sentence representation passed to the follow-up sub-tasks. Our experiments show that CMemN2N achieves better results on most of the 20 bAbI task dataset, yielding improvements for the average result compared to the state-of-the-art.

Variational Reasoning for Question Answering With Knowledge Graph

Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts. However, it is challenging to build QA systems which can learn to reason over knowledge graphs based on question-answer pairs alone. First, when people ask questions, their expressions are noisy (for example, typos in texts, or variations in pronunciations), which is non-trivial for the QA system to match those mentioned entities to the knowledge graph. Second, many questions require multi-hop logic reasoning over the knowledge graph to retrieve the answers. To address these challenges, we propose a novel and unified deep learning architecture, and an end-to-end variational learning algorithm which can handle noise in questions, and learn multi-hop reasoning simultaneously. Our method achieves state-of-the-art performance on a recent benchmark dataset in the literature. We also derive a series of new benchmark datasets, including questions for multi-hop reasoning, questions paraphrased by neural translation model, and questions in human voice. Our method yields very promising results on all these challenging datasets.