Explicit Graph Reasoning Fusing Knowledge and Contextual Information for Multi-hop Question Answering (2022)

Abstract: Current graph-neural-network-based (GNN-based) approaches to multi-hop questions integrate clues from scattered paragraphs in an entity graph, achieving implicit reasoning by synchronous update of graph node representations using information from neighbours; this is poorly suited for explaining how clues are passed through the graph in hops. In this paper, we describe a structured Knowledge and contextual Information Fusion GNN (KIFGraph) whose explicit multi-hop graph reasoning mimics human step by step reasoning. Specifically, we first integrate clues at multiple levels of granularity (question, paragraph, sentence, entity) as nodes in the graph, connected by edges derived using structured semantic knowledge, then use a contextual encoder to obtain the initial node representations, followed by step-by-step two-stage graph reasoning that asynchronously updates node representations. Each node can be related to its neighbour nodes through fused structured knowledge and contextual information, reliably integrating their answer clues. Moreover, a masked attention mechanism (MAM) filters out noisy or redundant nodes and edges, to avoid ineffective clue propagation in graph reasoning. Experimental results show performance competitive with published models on the HotpotQA dataset.

## introduction question answering (qa) is amongst the most commonly used tasks to quantify the reasoning and understanding ability of artificially intelligent systems. the performance of the most successful approaches on qa tasks such as squad @xcite , triviaqa @xcite , searchqa @xcite , now far exceeds that of humans @xcite . however, most studies on these tasks have focused on single-hop reasoning, where most questions can be answered with a single document and without complex reasoning. q: in which 2015 british-american romantic drama film directed by todd haynes did john magaro star in? selected paragraphs: p1 title: john magaro s1 john robert magaro @xcite ) is an american film, …… s2 he starred alongside james gandolfini in "not fade away" (2012), …... s3 he also starred alongside rooney mara in "carol" @xcite . for complex multi-hop reasoning, requiring multiple steps, these prior tasks provide a poor test. the multi-hop hotpotqa @xcite dataset, by contrast, is designed for systems that integrate information from multiple documents, reasoning to an answer explained using supporting facts. figure 1 is an example from hotpotqa, showing that structured knowledge, i.e., co-references and rdf triples, are useful in complex multi-hop reasoning. to answer the question, the multi-hop qa model must first use the coreference in which he in s2 refers to john robert magaro in s1, allowing it to integrate (he, star in, carol) with questionrelated structured knowledge (carol, directed by, todd haynes), thus reaching the final answer carol. most multi-hop qa models extract entities related to the question to construct an entity graph, and then apply a gnn-based model to integrate information across nodes and predict answers @xcite @xcite . however, this kind of gnnbased approach leaves challenges. first, existing graph construction methods do not precisely capture the semantic relationships between nodes, leading to unreliable integration of neighbouring nodes' information during graph reasoning. figure 1 shows how essential such structured semantic knowledge is in multi-hop reasoning. thus, integrating the semantics of input documents within gnn-based qa models remains a critical challenge. moreover, graph reasoning over noisy or redundant nodes and edges may lead to ineffective information integration, of e.g., the spurious (he, star in, not fade away) in s1. this necessitates filtering out such "noise" nodes and edges unrelated to the question. second, most multi-hop qa models combine all clues related to the question into a graph, and then apply gnn-based inference to update all node representations synchronously without considering the order of clues in the reasoning chains. in this type of approach, it is difficult to explain how the models make decisions and how clues are passed through the graph in hops @xcite . in this paper, we propose a structure knowledge and contextual information fusion gnn for multihop qa. our approach involves three steps: clue extraction, clue reasoning, and multi-task prediction. for clue extraction, we extract clues at multiple levels of granularity (question, paragraph, sentence, entity) as nodes, connected by semantic edges derived using structured knowledge. the motivation is that semantic edges provide more reliable information about neighbouring nodes for graph reasoning, compared to manually defined graph construction rules @xcite . for clue reasoning, inspired by step-by-step reasoning from cogqa @xcite , we first initialize all node representations using a pretrained contextual encoder, and then mimic human-like step-by-step reasoning via asynchronous update of node representations on the semantic graph. this update is a two-stage process in which nodes directly related to the question are updated first as direct clues, e.g., entities todd haynes and john magaro in figure 1 , followed by the remaining nodes which are updated as indirect clues, e.g., coral. at this point, we also apply a masked attention module to filter out noisy or spurious nodes and edges to avoid ineffective or deleterious clue propagation during gnn inference. finally, the updated node representations are passed to a multi-task layer that predicts the final answer, the answer type, supporting facts and an interpretable reasoning chain. we evaluated our proposed kifgraph on hot-potqa dataset and achieved a high rank amongst published systems on the leaderboard. the main contributions of this paper are as follows: hence, kifgraph foot_0 provides a new perspective on how to perform global interpretable reasoning through gnn-based methods, and achieves competitive performance on the hotpotqa benchmark. question decomposition for multi-hop qa. recent studies have focused on decomposing multihop questions into single-hop sub-questions, enabling existing single-hop qa models to be applied. decomprc @xcite uses a pointer model to split the question and generate sub-questions, and then answers these sub-questions using an existing single-hop qa model. qdmr @xcite trains a seq-to-seq model to parse multi-hop questions into a sequence of query steps. these qa systems attempt to find the essential clue for each sub-question, but largely ignore any relationships between the sub-questions. graph neural networks for multi-hop qa. motivated by work with gcns @xcite , recent studies have proposed that one should construct entity graphs from relevant paragraphs and apply gcns to perform implicit reasoning by propagating contextual information along graph edges. entity-gcn @xcite , mhqa-grn @xcite , coref-grn @xcite and dfgn @xcite select entity nodes and use rules to construct edges in the entity graphs. hde-graph @xcite and hgn @xcite construct a heterogeneous graph at multiple levels of granularity to maximise direct propagation of information via graph edges. ## methodology in this section, we describe in overview the kif-graph model in figure 2 . kifgraph involves the following three steps: i) clue extraction, including use of a paragraph retrieval module and a semantic graph construction module; ii) clue reasoning, including the masked attention and two-stage graph reasoning module at the centre of the figure; and iii) multi-task prediction, including answerspan prediction, answer-type prediction, supporting facts prediction and reasoning chain generation. ## conclusion and future work in this paper, we apply explicit graph reasoning to extracted knowledge and contextual information for multi-hop reasoning. we extract clues at multiple levels of granularity relating entity nodes, and construct a semantic graph from these clues. we then combine a masked attention mechanism and two-stage graph reasoning to perform interpretable inference over the semantic graph. experimental results on hotpotqa dataset show the effectiveness of our model. in future work, we hope to extend the range and precision of the entity relations used, and we hope to extend our model to accommodate more complex multi-hop questions with unknown number of hops and non-linear reasoning.