| Challenge: | a new framework for multi-hop reading comprehension question answering is needed to cross the chasm of reading comprehension between machine and human. |
| Approach: | They propose a CogQA framework for multi-hop reading comprehension question answering in web-scale documents that builds a cognitive graph in an iterative process by coordinating an implicit extraction module and an explicit reasoning module. |
| Outcome: | The proposed framework outperforms the best competitor in the hotpotQA dataset in F1 . it provides explainable reasoning paths and accurate answers, while giving accurate answers . |
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Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs (P19-1)
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| Challenge: | Existing models to tackle multi-hop reading comprehension (RC) are focusing on a single document or paragraph, but they lack the ability to do reasoning across multiple documents. |
| Approach: | They propose a heterogeneous document-entity graph with different types of nodes and edges to solve multi-hop RC problem. |
| Outcome: | The proposed model can do reasoning over the proposed graph with nodes representation initialized with co-attention and self-attention based context encoders. |
Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs (2021.findings-acl)
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| Challenge: | Knowledge Graphs (KGs) store structured human knowledge with nodes and edges being entities and relations between them. |
| Approach: | They propose a deep cognitive reasoning network that uses two phases to find answers in large candidate entity sets. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on benchmark datasets. |
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2020.emnlp-main)
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| Challenge: | Existing work on augmenting question answering models with external knowledge (e.g., knowledge graphs) lacks transparency into the model’s prediction rationale. |
| Approach: | They propose a knowledge-aware approach that equips pre-trained language models with a multi-hop relational reasoning module that performs multi-relational reasoning over subgraphs extracted from external knowledge graphs. |
| Outcome: | The proposed model performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs. |
Exploiting Explicit Paths for Multi-hop Reading Comprehension (P19-1)
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| Challenge: | Existing approaches to multi-hop reading comprehension do not include multiple sentences or passages. |
| Approach: | They propose a path-based reasoning approach for a multi-hop reading comprehension task . they propose to extract paths from text and compose them to encode them . |
| Outcome: | The proposed model outperforms previous models on the multi-hop Wikihop dataset and can be generalized to the OpenBookQA dataset. |
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks (D19-53)
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| Challenge: | Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. |
| Approach: | They propose a document-structured message passing architecture for the identification of supporting facts over a graph-structure based representation of text. |
| Outcome: | The proposed model outperforms a baseline reading comprehension test on raw text and shows that it is relevant for multi-hop reasoning. |
Is Graph Structure Necessary for Multi-hop Question Answering? (2020.emnlp-main)
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| Challenge: | Existing studies focus on multi-hop question answering across multiple documents or paragraphs. |
| Approach: | They propose a graph neural network to deal with graph structure in textual multi-hop reasoning . they propose 'self-attention' and propose removing entire graph structure may not hurt the final results . |
| Outcome: | The proposed model shows that graph-attention or the entire graph structure can be replaced by self-attention . hotpotQA is a widely used benchmark for multi-hop question answering . |
Question Answering by Reasoning Across Documents with Graph Convolutional Networks (N19-1)
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| Challenge: | Recent research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. |
| Approach: | They propose a neural model which integrates and reasons relying on information spread within documents and across multiple documents. |
| Outcome: | The proposed model achieves state-of-the-art on a multi-document question answering dataset, WikiHop. |
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs (2022.coling-1)
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| Challenge: | Existing approaches to answer natural language questions on knowledge graphs (KGQA) use large-scale entity-related text corpus or knowledge graph embeddings as auxiliary information to facilitate answer selection. |
| Approach: | They propose to integrate explicit textual information and implicit KG structural features of relation paths into a novel rotate-and-scale entity link prediction framework. |
| Outcome: | The proposed method is superior to existing methods on three KGQA datasets and shows that it can be used to identify answer entities. |
GLGR: Question-aware Global-to-Local Graph Reasoning for Multi-party Dialogue Reading Comprehension (2023.findings-emnlp)
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| Challenge: | Existing approaches for multi-hop reasoning are lacking for local graph reasoning . existing approaches neglect local semantic structures in utterances . |
| Approach: | They propose a question-aware global-to-local graph reasoning approach that expands the canonical Interlocutor-Utterance graph by introducing a query node. |
| Outcome: | The proposed approach outperforms existing methods on Molweni and FriendsQA. |
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering (2021.findings-acl)
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| Challenge: | Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process. |
| Approach: | They propose a framework to exploit more valid facts while obtaining explainability for multi-hop question answering at web scale by dynamically constructing a semantic graph and reasoning over it. |
| Outcome: | The proposed framework surpasses existing approaches while maintaining high explainability on OpenBookQA and ARC-Challenge. |