Modeling Multi-hop Question Answering as Single Sequence Prediction (2022.acl-long)
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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. |
| Outcome: | The proposed model improves on two multi-hop QA datasets and is interpretable. |
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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 . |
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| Challenge: | Experimental results show that Transformer-based questions have a "long" hybrid sequence over tabular and textual elements, causing long-range reasoning problems. |
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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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| Challenge: | Open-domain question answering requires deriving factual responses without explicit evidence . recent approaches combine retrieval of relevant information with response generation . |
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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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| Challenge: | Empirical evaluation shows our model to outperform the single-hop question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions. |
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Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering (2025.findings-acl)
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Binquan Ji, Haibo Luo, YifeiLu YifeiLu, Lei Hei, Jiaqi Wang, Tingjing Liao, Wang Lingyu, Shichao Wang, Feiliang Ren
| Challenge: | Existing approaches to solve multi-hop question answering challenges require multiple rounds of retrieval and iterative generation. |
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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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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. |
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