Relevant C ommon S ense Subgraphs for “What if...” Procedural Reasoning (2022)

Abstract: We study the challenge of learning causal reasoning over procedural text to answer “What if...” questions when external commonsense knowledge is required. We propose a novel multi-hop graph reasoning model to 1) efficiently extract a commonsense subgraph with the most relevant information from a large knowledge graph; 2) predict the causal answer by reasoning over the representations obtained from the commonsense subgraph and the contextual interactions between the questions and context. We evaluate our model on WIQA benchmark and achieve state-of-the-art performance compared to the recent models.

## introduction in recent years, large-scale pre-trained language models (lms) have made a breakthrough progress and demonstrate a high performance in many nlp tasks, including procedural text reasoning @xcite . there is a large amount of knowledge that is stored implicitly in language models that help in solving various nlp tasks @xcite . when we reason over text, sometimes, the knowledge contained in a given text is sufficient to predict the answer, as it is shown in the question 1 of figure 1 . this knowledge is directly encoded and used by lms models @xcite . however, there are many cases in which the required knowledge is not included in the procedural text itself. for example, for the question 2 in figure 1 , the information about the "nutrient" on the seeds does not exist in the procedural text. therefore, the external commonsense knowledge is required. there are several existing resources that contain world knowledge and commonsense. examples are knowledge graphs (kgs) like conceptnet @xcite and atomic @xcite . looking back at the question 2, we observe that through providing the external knowledge triplets (nutrient, procedural text: 1. a plant produces a seed. 2. the seed falls to the ground. 3. the seed is buried. 4. the seed germinates. 5. a plant grows. 6. the plant produces flowers. 7. the flowers produce more seeds ## experiments and results we implemented our mrrg framework using py-torch 1 . we use a pre-trained roberta @xcite to encode the contextual information in the input. the maximum number of triplets is 50 and the maximum number of nodes in the graph is 100. further details of hyper-parameters of the graph are shown in ## conclusion we propose mrrg model for using external knowledge graph in reasoning over procedural text. our model extracts a relevant subgraph for each question from the kg and uses that knowledge subgraph for answering the question. the extracted subgraph includes the reasoning path for answering the question and helps multi-hop reasoning to predict an explainable answer. we evaluate mrrg on the wiqa and achieve sota performance.