@inproceedings{xiang-etal-2021-three,
title = "A Three-step Method for Multi-Hop Inference Explanation Regeneration",
author = "Xiang, Yuejia and
Zhang, Yunyan and
Shi, Xiaoming and
Liu, Bo and
Xu, Wandi and
Chen, Xi",
editor = "Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Logacheva, Varvara and
Jana, Abhik and
Ustalov, Dmitry and
Jansen, Peter",
booktitle = "Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.textgraphs-1.19",
doi = "10.18653/v1/2021.textgraphs-1.19",
pages = "171--175",
abstract = "Multi-hop inference for explanation generation is to combine two or more facts to make an inference. The task focuses on generating explanations for elementary science questions. In the task, the relevance between the explanations and the QA pairs is of vital importance. To address the task, a three-step framework is proposed. Firstly, vector distance between two texts is utilized to recall the top-K relevant explanations for each question, reducing the calculation consumption. Then, a selection module is employed to choose those most relative facts in an autoregressive manner, giving a preliminary order for the retrieved facts. Thirdly, we adopt a re-ranking module to re-rank the retrieved candidate explanations with relevance between each fact and the QA pairs. Experimental results illustrate the effectiveness of the proposed framework with an improvement of 39.78{\%} in NDCG over the official baseline.",
}
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<abstract>Multi-hop inference for explanation generation is to combine two or more facts to make an inference. The task focuses on generating explanations for elementary science questions. In the task, the relevance between the explanations and the QA pairs is of vital importance. To address the task, a three-step framework is proposed. Firstly, vector distance between two texts is utilized to recall the top-K relevant explanations for each question, reducing the calculation consumption. Then, a selection module is employed to choose those most relative facts in an autoregressive manner, giving a preliminary order for the retrieved facts. Thirdly, we adopt a re-ranking module to re-rank the retrieved candidate explanations with relevance between each fact and the QA pairs. Experimental results illustrate the effectiveness of the proposed framework with an improvement of 39.78% in NDCG over the official baseline.</abstract>
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%0 Conference Proceedings
%T A Three-step Method for Multi-Hop Inference Explanation Regeneration
%A Xiang, Yuejia
%A Zhang, Yunyan
%A Shi, Xiaoming
%A Liu, Bo
%A Xu, Wandi
%A Chen, Xi
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Logacheva, Varvara
%Y Jana, Abhik
%Y Ustalov, Dmitry
%Y Jansen, Peter
%S Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F xiang-etal-2021-three
%X Multi-hop inference for explanation generation is to combine two or more facts to make an inference. The task focuses on generating explanations for elementary science questions. In the task, the relevance between the explanations and the QA pairs is of vital importance. To address the task, a three-step framework is proposed. Firstly, vector distance between two texts is utilized to recall the top-K relevant explanations for each question, reducing the calculation consumption. Then, a selection module is employed to choose those most relative facts in an autoregressive manner, giving a preliminary order for the retrieved facts. Thirdly, we adopt a re-ranking module to re-rank the retrieved candidate explanations with relevance between each fact and the QA pairs. Experimental results illustrate the effectiveness of the proposed framework with an improvement of 39.78% in NDCG over the official baseline.
%R 10.18653/v1/2021.textgraphs-1.19
%U https://aclanthology.org/2021.textgraphs-1.19
%U https://doi.org/10.18653/v1/2021.textgraphs-1.19
%P 171-175
Markdown (Informal)
[A Three-step Method for Multi-Hop Inference Explanation Regeneration](https://aclanthology.org/2021.textgraphs-1.19) (Xiang et al., TextGraphs 2021)
ACL
- Yuejia Xiang, Yunyan Zhang, Xiaoming Shi, Bo Liu, Wandi Xu, and Xi Chen. 2021. A Three-step Method for Multi-Hop Inference Explanation Regeneration. In Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15), pages 171–175, Mexico City, Mexico. Association for Computational Linguistics.