| 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. |
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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. |
Cognitive Graph for Multi-Hop Reading Comprehension at Scale (P19-1)
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| 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. |
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Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)
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| Challenge: | Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA. |
| Approach: | They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer. |
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Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering (2022.coling-1)
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| Challenge: | Existing methods for multi-hop reasoning ignore grounding on supporting facts of each step, which tends to generate inaccurate decompositions. |
| Approach: | They propose an interpretable stepwise reasoning framework that incorporates supporting sentences and questions at each intermediate step and utilizes the inference of the current hop for the next until reasoning out the final result. |
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Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing approaches that integrate commonsense knowledge into pre-trained language models simply transfer relational knowledge while ignoring rich connections within the knowledge graph. |
| Approach: | They propose a method that leverages structural and semantic information of the knowledge graph to generate commonsense-aware text. |
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Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension (P19-1)
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| Challenge: | Existing models for multi-hop reading comprehension only require a single-hop reasoning, meaning that the evidence needed to answer the question is scattered in a set of supporting documents. |
| Approach: | They propose an interpretable 3-module system called Explore-Propose-Assemble reader (EPAr) that explores and connects relevant information from multiple documents in order to answer a question about the context. |
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Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering (D19-58)
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| Challenge: | Existing work on open-domain multi-hop question answering relies on off-the-shelf information retrieval techniques to retrieve answer passages. |
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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. |
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Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps (2020.coling-main)
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| Challenge: | Existing multi-hop question answering datasets do not provide a complete explanation for the reasoning process from the question to the answer. |
| Approach: | They propose a multi-hop question answering dataset that uses structured and unstructured data to test reasoning skills. |
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Triggering Multi-Hop Reasoning for Question Answering in Language Models using Soft Prompts and Random Walks (2023.findings-acl)
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| Challenge: | Existing methods that decompose multi-hop questions into single hop sub-questions are difficult to implement. |
| Approach: | They propose to use random-walks to guide pre-trained language models to map multi-hop questions to random-walked paths that lead to the answer. |
| Outcome: | The proposed methods improve on two T5 LMs. |