Single Sequence Prediction over Reasoning Graphs for Multi-hop QA (2023.acl-long)
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| Challenge: | Recent generative approaches for multi-hop question answering (QA) use fusion-in-decoder to generate a single sequence output . but, they often have difficulty accurately identifying passages corresponding to key entities in the context . |
| Approach: | They propose a single-sequence prediction method that integrates a graph structure linking key entities in each context passage to relevant subsequent passages for each question. |
| Outcome: | The proposed method improves answer exact-match/F1 scores and faithfulness of grounding on the hotpotQA dataset and achieves state-of-the-art numbers on the Musique dataset. |
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| Challenge: | Existing generative question answering models that leverage passage retrieval with a pre-trained transformer are not effective for multihop QA. |
| Approach: | They propose a generative approach that explicitly models the reasoning process to resolve the answer for multi-hop questions by encoding cross-passage interactions. |
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| Challenge: | Existing studies on text-based QG focus on generating SQuAD-style questions. |
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Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions? (2021.eacl-main)
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| Challenge: | Existing models fail to answer a large portion of sub-questions . Existing systems have achieved super-human performance . |
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SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning (2022.emnlp-main)
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| Challenge: | Existing methods for multi-hop knowledge graph reasoning suffer from slow and poor convergence . a transformer model can be used to learn and predict in an end-to-end fashion, giving faster convergence compared to previous methods . |
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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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Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering (2022.coling-1)
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| Challenge: | Generative question answering (QA) models generate answers to complex questions, but their mechanism for doing so is still poorly understood. |
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Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning (D19-1)
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| Challenge: | Existing models for multi-hop question answering require multiple pieces of evidence scattered in a given context. |
| Approach: | They propose an interpretable, controller-based self-assembling Neural Modular Network for multi-hop reasoning . their model can softly decompose a multi-step question into multiple single-hop sub-questions . |
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Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning (2024.findings-acl)
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| Challenge: | Existing methods rely on linear sequential operations to solve First-Order Logic queries. |
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Breadth First Reasoning Graph for Multi-hop Question Answering (2021.naacl-main)
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| Challenge: | Recent Graph Neural Network (GNN) has been used as a promising tool in multi-hop question answering task. |
| Approach: | They propose a model of Breadth First Reasoning Graph that passes to next nodes hop by hop until all edges have been passed. |
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