| Challenge: | a single-hop reasoning model can solve much more of the dataset than previously thought. |
| Approach: | They propose a single-hop BERT-based RC model that achieves 67 F1 . they propose an evaluation setting where humans are not shown all paragraphs . |
| Outcome: | The proposed model achieves 67 F1—comparable to state-of-the-art multi-hop models. |
Similar Papers
Multi-hop Reading Comprehension through Question Decomposition and Rescoring (P19-1)
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| Challenge: | Existing systems for multi-hop reading comprehension decompose compositional questions into simpler sub-questions . authors propose a system that learns to break compositional multi- hop questions into simple singlehop sub-question . |
| Approach: | They propose a system that decomposes a compositional question into simpler sub-questions . they propose recast subquestion generation as a span prediction problem . |
| Outcome: | The proposed system generates as effective as human-authored sub-questions using 400 examples . it also provides explainable evidence for its decision making in the form of sub-questions . |
Do Multi-hop Readers Dream of Reasoning Chains? (D19-58)
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| Challenge: | Existing models for multihop reasoning are limited in their performance . multi-hop reasoning requires the ability to gather information from multiple passages . |
| Approach: | They propose a method that provides the full reasoning chain of multiple passages instead of just one final passage where the answer appears. |
| Outcome: | The proposed model improves on existing models by providing the full reasoning chain of multiple passages instead of just one final passage where the answer appears. |
Understanding Dataset Design Choices for Multi-hop Reasoning (N19-1)
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| Challenge: | Existing datasets that explicitly focus on multi-hop reasoning are lacking in learning multi-tasking. |
| Approach: | They propose to use sentence-factored models to solve multi-hop question answering tasks . they find spurious correlations in unmasked versions of WikiHop and HotpotQA . |
| Outcome: | The proposed datasets are used to test models on multi-hop question answering tasks. |
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. |
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. |
| Outcome: | The proposed models lack zero-shot multi-hop reasoning ability when trained on single-hop questions and on logical forms. |
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. |
| Outcome: | The proposed model can boost performance and yield a better interpretable reasoning process without decomposition supervision. |
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop? (2022.coling-1)
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| Challenge: | Recent developments have shown that pre-trained language models are effective soft reasoners over language. |
| Approach: | They propose to model multi-hop reasoning process as a sequence of explicit single-hop steps. |
| Outcome: | The proposed model improves on multiple-choice question answering and reading comprehension with 68.4% and 16.0% w.r.t. classic PLMs. |
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension (2021.emnlp-main)
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| Challenge: | Recent approaches to multi-hop Reading Comprehension (RC) have greatly improved its explainability, models ability to explain their own answers. |
| Approach: | They propose to generate a question-focused abstractive summary of input paragraphs and feed it to an RC system. |
| Outcome: | The proposed explanation generates more compact explanations than an extractive explainer with limited supervision while maintaining sufficiency. |
Low-Resource Generation of Multi-hop Reasoning Questions (2020.acl-main)
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| Challenge: | Existing methods to generate valid and fluent questions from text are limited and insufficient for training. |
| Approach: | They propose to generate multi-hop reasoning questions from the raw text in a low resource circumstance by deducing over multiple relations on several sentences in the text. |
| Outcome: | The proposed model can be applied to the task of machine reading comprehension and achieve significant performance improvements. |
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 . |
| Approach: | They propose to use a neural decomposition model to generate sub-questions for a multi-hop question and extract the corresponding sub-answers. |
| Outcome: | The proposed model is based on a hotpotQA dataset with a multi-hop question and sub-answers. |