| Challenge: | Existing methods for weakly supervised multi-hop pretraining require costly human annotation. |
| Approach: | They propose a method for weakly supervised multi-hop retriever pretraining without human efforts by generating vector representations of complex questions and subquestion as weak supervision for pre-training. |
| Outcome: | The proposed method is effective and robust on limited data and computational resources. |
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| Challenge: | Existing beam retrieval frameworks for multi-hop question answering were customized for two-hop questions and were poorly supervised. |
| Approach: | They propose an end-to-end beam retrieval framework for multi-hop question answering . they combine an encoder and two classification heads to optimize the retrieval process . |
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| Challenge: | Existing methods to generate valid and fluent questions from text are limited and insufficient for training. |
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Unsupervised Multi-hop Question Answering by Question Generation (2021.naacl-main)
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| Challenge: | Existing training data for multi-hop question answering (QA) is time-consuming and resource-intensive. |
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| Challenge: | Existing approaches to robustify multi-hop question answering models require expensive annotations. |
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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. |
| Approach: | They decompose multi-hop questions into multiple corresponding single-hop question chains and find marked inconsistency in QA models’ answers on these pairs of ostensibly identical question chains. |
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Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering (2022.acl-long)
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Generative Multi-hop Retrieval (2022.emnlp-main)
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Generative Context Pair Selection for Multi-hop Question Answering (2021.emnlp-main)
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Dheeru Dua, Cicero Nogueira dos Santos, Patrick Ng, Ben Athiwaratkun, Bing Xiang, Matt Gardner, Sameer Singh
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Hypergraph Transformer: Weakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering (2022.acl-long)
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| Challenge: | Existing knowledge-based visual question answering tasks require weak supervision and no visual knowledge. |
| Approach: | They propose a model which encodes high-level semantics of a question and a knowledge base and learns high order associations between them. |
| Outcome: | The proposed model encodes high-level semantics of a question and a knowledge base, and learns high order associations between them. |