Exploiting Explicit Paths for Multi-hop Reading Comprehension

Abstract

We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question. Although inspired by multi-hop reasoning over knowledge graphs, our proposed approach operates directly over unstructured text. It generates potential paths through passages and scores them without any direct path supervision. The proposed model, named PathNet, attempts to extract implicit relations from text through entity pair representations, and compose them to encode each path. To capture additional context, Path-Net also composes the passage representations along each path to compute a passage-based representation. Unlike previous approaches, our model is then able to explain its reasoning via these explicit paths through the passages. We show that our approach outperforms prior models on the multi-hop Wikihop dataset, and also can be generalized to apply to the OpenBookQA dataset, matching stateof-the-art performance. * Work performed while doing an internship at the Allen Institute for Artificial Intelligence. Recently, graph-based models (Song et al., 2018;Cao et al., 2018) have been proposed for the Wik-iHop dataset (Welbl et al., 2018). Nevertheless, these models still only implicitly combine knowl-

Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs

“…In Table 1, we show the results of the our proposed HDE graph based model on both development and test set and compare it with previously published results. We show that our proposed HDE graph based model improves the published state-of-the-art accuracy on development set from 67.1% (Kundu et al, 2018) to 68.1%, on the blind test set from 70.6% (Zhong et al, 2019) to 70.9%. Compared to the best single model "DynSAN" (unpublished) on WIKIHOP leaderboard, our proposed model is still 0.5% worse.…”

“…The study presented in this paper is directly related to existing research on multi-hop reading comprehension across multiple documents (Dhingra et al, 2018;Song et al, 2018;De Cao et al, 2018;Zhong et al, 2019;Kundu et al, 2018). The method presented in this paper is similar to previous studies using GNN for multi-hop reasoning (Song et al, 2018;De Cao et al, 2018).…”

“…In the future, we would like to investigate explainable GNN for this task, such as explicit reasoning path in (Kundu et al, 2018), and work on other data sets such as HotpotQA.…”

A Sentence-Based Circular Reasoning Model in Multi-Hop Reading Comprehension

“…With the assistance of knowledge guidance, [19] enables the model to integrate the semantics of documents, but the approach is difficult to extend, since the external knowledge tends to be limited to a specific field. Focused on reasoning, [10] gathers all possible reasoning paths based on the entities contained in the documents, and then scores all paths to identify the correct one. However, this method would extract many invalid paths, thus bringing in interference and wasting the computing resources.…”

“…In view of these limitations, this study abandons the current mainstream multi-hop inference methods based on document levels [6]- [8] or entity levels [9], [10]. Rather, it will choose to hop through sentences and gradually build an explicit path based on sentences, so as to deliver the jumps among semantics.…”