Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference (D19-53)
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| Challenge: | EMNLP 2019 shared task on 'Multi-hop Inference Explanation Regeneration' identifies chains of facts relevant to explain an answer to an elementary science examination question. |
| Approach: | They propose a system that identifies chains of facts relevant to explain an answer to an elementary science examination question. |
| Outcome: | The proposed system outperforms the second best system by 14.95 points on the mean average precision (MAP) metric. |
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TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration (D19-53)
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| Challenge: | Detailed extended analyses of all submitted systems showed large relative improvements in accessing the most challenging multi-hop inference problems, while absolute performance remains low. |
| Approach: | The Shared Task on Multi-Hop Inference for Explanation Regeneration asks participants to regenerate detailed gold explanations for elementary science questions by selecting facts from a knowledge base of semi-structured tables. |
| Outcome: | The top-performing system achieved a mean average precision of 0.56 . the task combines facts from a knowledge base and supervised training data . |
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking (D19-53)
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| Challenge: | Explanation Regeneration task is an intermediate step towards general multi-hop inference on large graphs. |
| Approach: | They propose a system that performs multi-hop inference and ranks a set of explanatory facts for a given elementary science question and correct answer pair. |
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Autoregressive Reasoning over Chains of Facts with Transformers (2020.coling-main)
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| Challenge: | Existing work on this task either evaluates facts in isolation or artificially limits the possible chains of facts, thus limiting multi-hop inference. |
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WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference (2020.lrec-1)
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Zhengnan Xie, Sebastian Thiem, Jaycie Martin, Elizabeth Wainwright, Steven Marmorstein, Peter Jansen
| Challenge: | Standardized science questions require combining an average of 6 facts and as many as 16 facts to answer and explain. |
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Extracting Common Inference Patterns from Semi-Structured Explanations (D19-60)
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| Challenge: | Multi-hop inference suffers from semantic drift, or the tendency for chains of reasoning to "drift"' to unrelated topics. |
| Approach: | They propose to extract large high-confidence multi-hop inference patterns from a corpus of explanations by abstracting large-scale structure from logical sentences. |
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Unification-based Reconstruction of Multi-hop Explanations for Science Questions (2021.eacl-main)
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| Challenge: | Existing approaches build explanations considering each question in isolation, but new approach leverages explanatory patterns emerging in scientific explanations. |
| Approach: | They propose a framework for reconstructing multi-hop explanations in science Question Answering . they integrate lexical relevance with the notion of unification power to rank atomic facts . |
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Explainable Inference Over Grounding-Abstract Chains for Science Questions (2021.findings-acl)
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| Challenge: | Existing inference models for science questions are black-box by nature, lacking explanations for their predictions. |
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Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions (D19-53)
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| Challenge: | TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration tackles explanation generation for elementary science questions. |
| Approach: | They propose a hybrid pipelined machine learning model and rule-based system to address MIER-19 . they use a featurerich learning-to-rank machine learning and a rule-driven system to rerank the LTR model predictions. |
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WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference (L18-1)
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| Challenge: | Existing methods of automated inference do not provide enough gold explanations to train models . standardized science exams are a challenge task for question answering . |
| Approach: | They propose to manually construct a corpus of explanations for standardized science exams . they also provide an explanation-centered tablestore that contains the knowledge to construct these explanations . |
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Exploiting Reasoning Chains for Multi-hop Science Question Answering (2021.findings-emnlp)
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| Challenge: | Existing frameworks for multi-hop Science question answering do not require corpus-specific annotations. |
| Approach: | They propose a chain-guided retriever-reader framework that performs explainable reasoning without corpus annotations. |
| Outcome: | The proposed framework performs explainable reasoning without corpus-specific annotations . it is shown to be effective on OpenBookQA and ARC-Challenge . |