Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph (2020)

Abstract: Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing approaches that integrate commonsense knowledge into generative pre-trained language models simply transfer relational knowledge by post-training on individual knowledge triples while ignoring rich connections within the knowledge graph. We argue that exploiting both the structural and semantic information of the knowledge graph facilitates commonsense-aware text generation. In this paper, we propose Generation with Multi-Hop Reasoning Flow (GRF) that enables pre-trained models with dynamic multi-hop reasoning on multi-relational paths extracted from the external commonsense knowledge graph. We empirically show that our model outperforms existing baselines on three text generation tasks that require reasoning over commonsense knowledge. We also demonstrate the effectiveness of the dynamic multi-hop reasoning module with reasoning paths inferred by the model that provide rationale to the generation.

## introduction despite the recent success of pre-trained language models such as gpt-2 @xcite on various language generation tasks, these models are still struggling on generation tasks that require reasoning over commonsense knowledge that is not explicitly stated in the context. for example, figure 1 illustrates an example in the story ending generation task, where external commonsense knowledge in the form of relational paths can guide the generation of the key concepts "substance" and eruption mr. egg was presenting a volcanic eruption to the science class. he has a diagram of a volcano that looked like it was made of tinfoil. he then took out a huge thing of vinegar and started to pour it in! story context science volcano thing pour water rock earth substance lava relational paths r el at ed to r e la te d t o r e la te d to ma deo f atl oca tion r e la te d to is a the volcano then exploded with substance that looked like lava ! ## conclusion we present generation with multi-hop reasoning flow that reasons over structured commonsense knowledge during text generation. the proposed method leverages both the structural and semantic information of the external knowledge base by performing dynamic multi-hop reasoning on the relational paths. we conduct extensive experiments and empirically show that our method outperforms existing approaches that integrate commonsense knowledge to pre-trained language models on three text generation tasks. we also demonstrate the interpretability of our method with inferred reasoning paths that provide rationale to the generated results.