Generating Commonsense Explanation by Extracting Bridge Concepts from Reasoning Paths (2020)

Abstract: Commonsense explanation generation aims to empower the machine’s sense-making capability by generating plausible explanations to statements against commonsense. While this task is easy to human, the machine still struggles to generate reasonable and informative explanations. In this work, we propose a method that first extracts the underlying concepts which are served as bridges in the reasoning chain and then integrates these concepts to generate the final explanation. To facilitate the reasoning process, we utilize external commonsense knowledge to build the connection between a statement and the bridge concepts by extracting and pruning multi-hop paths to build a subgraph. We design a bridge concept extraction model that first scores the triples, routes the paths in the subgraph, and further selects bridge concepts with weak supervision at both the triple level and the concept level. We conduct experiments on the commonsense explanation generation task and our model outperforms the state-of-the-art baselines in both automatic and human evaluation.

## introduction machine commonsense reasoning has been widely acknowledged as a crucial component of artificial intelligence and a considerable amount of work has been dedicated to evaluate this ability from various aspects in natural language processing @xcite @xcite . a large proportion of existing tasks frame commonsense reasoning as multi-choice reading comprehension problems, which lack direct assessment to machine commonsense @xcite and impede its practicability to realistic scenarios @xcite figure 1 : generating a reasonable and informative explanation involves generating bridge concepts like vacation by identifying the relation to the source concepts, i.e. school and summer in the statement. @xcite . recently, @xcite proposed a commonsense explanation generation challenge that directly tests machine's sense-making capability via commonsense reasoning. in this paper, we focus on the challenging explanation generation task where the goal is to generate a sentence to explain the reasons why the input statement is against commonsense, as shown in figure 1 . generating a reasonable explanation for a statement faces two main challenges: 1) trivial and uninformative explanations. as this task can be formulated as a sequence-to-sequence generation task, existing neural language generation models tend to generate trivial and uninformative explanations. for example, one of the existing neural models generates an explanation "the school wasn't open for summer" to the statement in figure 1 . although it is sometimes reasonable, simple modification of the statement to the negation form with no additional information cannot explain the reasons why the statement conflicts with commonsense. 2) noisy commonsense knowledge grounding. it's still challenging for most existing language generation models to generate explanations that are faithful to commonsense @xcite . thus, explicitly incorporating external knowledge sources is necessary for this task. since the nature of the explanation generation task involves using underlying commonsense knowledge to explain, locating useful commonsense knowledge from large-scale knowledge graph is not trivial and generally requires multi-hop reasoning. to address the above challenges, we propose a two-stage generation framework that first extracts the critical concepts served as bridges between the statement and the explanation from an external commonsense knowledge graph, and then generates plausible explanations with these concepts. we first retrieve multi-hop reasoning paths from conceptnet @xcite and heuristically prune the paths to maintain the coverage to plausible concepts while keeping the scale of the subgraph tractable. before the extraction stage, we initialize the representation of each node on the subgraph by fusing both the contextual and graph information. then, we design a bridge concept extraction model that scores triples, propagates the probabilities along multi-hop paths to the connected concepts and further extracts plausible concepts. in the second stage, we use a pre-trained language model @xcite to generate the explanation by integrating both the statement and the extracted concept representations. experimental results show that our framework outperforms knowledge-aware text generation baselines and gpt-2 @xcite in both automatic and human evaluation. particularly, our model generates explanations with more informative content and provides reasoning paths on the knowledge graph for concept extraction. to summarize, our contributions are two-fold: ## conclusion in this paper, we analyze the challenges in incorporating external knowledge graph to aid the commonsense generation problem and propose a twostage method that first extracts bridge concepts from a retrieved subgraph and then generates the explanation by integrating the extracted concepts. experimental results show that our model outperforms baselines including the strong pre-trained language model gpt-2 in both automatic and manual evaluation.