@inproceedings{pan-etal-2021-deepblueai,
title = "{D}eep{B}lue{AI} at {T}ext{G}raphs 2021 Shared Task: Treating Multi-Hop Inference Explanation Regeneration as A Ranking Problem",
author = "Pan, Chunguang and
Song, Bingyan and
Luo, Zhipeng",
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-1.18",
doi = "10.18653/v1/2021.textgraphs-1.18",
pages = "166--170",
abstract = "This paper describes the winning system for TextGraphs 2021 shared task: Multi-hop inference explanation regeneration. Given a question and its corresponding correct answer, this task aims to select the facts that can explain why the answer is correct for that question and answering (QA) from a large knowledge base. To address this problem and accelerate training as well, our strategy includes two steps. First, fine-tuning pre-trained language models (PLMs) with triplet loss to recall top-K relevant facts for each question and answer pair. Then, adopting the same architecture to train the re-ranking model to rank the top-K candidates. To further improve the performance, we average the results from models based on different PLMs (e.g., RoBERTa) and different parameter settings to make the final predictions. The official evaluation shows that, our system can outperform the second best system by 4.93 points, which proves the effectiveness of our system. Our code has been open source, address is \url{https://github.com/DeepBlueAI/TextGraphs-15}",
}
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<abstract>This paper describes the winning system for TextGraphs 2021 shared task: Multi-hop inference explanation regeneration. Given a question and its corresponding correct answer, this task aims to select the facts that can explain why the answer is correct for that question and answering (QA) from a large knowledge base. To address this problem and accelerate training as well, our strategy includes two steps. First, fine-tuning pre-trained language models (PLMs) with triplet loss to recall top-K relevant facts for each question and answer pair. Then, adopting the same architecture to train the re-ranking model to rank the top-K candidates. To further improve the performance, we average the results from models based on different PLMs (e.g., RoBERTa) and different parameter settings to make the final predictions. The official evaluation shows that, our system can outperform the second best system by 4.93 points, which proves the effectiveness of our system. Our code has been open source, address is https://github.com/DeepBlueAI/TextGraphs-15</abstract>
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%0 Conference Proceedings
%T DeepBlueAI at TextGraphs 2021 Shared Task: Treating Multi-Hop Inference Explanation Regeneration as A Ranking Problem
%A Pan, Chunguang
%A Song, Bingyan
%A Luo, Zhipeng
%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 pan-etal-2021-deepblueai
%X This paper describes the winning system for TextGraphs 2021 shared task: Multi-hop inference explanation regeneration. Given a question and its corresponding correct answer, this task aims to select the facts that can explain why the answer is correct for that question and answering (QA) from a large knowledge base. To address this problem and accelerate training as well, our strategy includes two steps. First, fine-tuning pre-trained language models (PLMs) with triplet loss to recall top-K relevant facts for each question and answer pair. Then, adopting the same architecture to train the re-ranking model to rank the top-K candidates. To further improve the performance, we average the results from models based on different PLMs (e.g., RoBERTa) and different parameter settings to make the final predictions. The official evaluation shows that, our system can outperform the second best system by 4.93 points, which proves the effectiveness of our system. Our code has been open source, address is https://github.com/DeepBlueAI/TextGraphs-15
%R 10.18653/v1/2021.textgraphs-1.18
%U https://aclanthology.org/2021.textgraphs-1.18
%U https://doi.org/10.18653/v1/2021.textgraphs-1.18
%P 166-170
Markdown (Informal)
[DeepBlueAI at TextGraphs 2021 Shared Task: Treating Multi-Hop Inference Explanation Regeneration as A Ranking Problem](https://aclanthology.org/2021.textgraphs-1.18) (Pan et al., TextGraphs 2021)
ACL