DeepBlueAI at TextGraphs 2021 Shared Task: Treating Multi-Hop Inference Explanation Regeneration as A Ranking Problem

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

[1]  Peter A. Jansen,et al.  WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference , 2020, LREC.

[2]  Rajarshi Das,et al.  Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference , 2019, EMNLP.

[3]  Clayton T. Morrison,et al.  WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference , 2018, LREC.

[5]  Yu Sun,et al.  PGL at TextGraphs 2020 Shared Task: Explanation Regeneration using Language and Graph Learning Methods , 2020, TEXTGRAPHS.

[6]  Omer Levy,et al.  RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.

[7]  Sam Witteveen,et al.  Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation , 2019, TextGraphs@EMNLP.

[8]  Dmitry Ustalov,et al.  TextGraphs 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration , 2020, TEXTGRAPHS.

[9]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Hao Tian,et al.  ERNIE 2.0: A Continual Pre-training Framework for Language Understanding , 2019, AAAI.

[11]  Oren Etzioni,et al.  Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge , 2018, ArXiv.

[12]  Dmitry Ustalov,et al.  TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration , 2019, EMNLP.

[13]  Marco Valentino,et al.  A Survey on Explainability in Machine Reading Comprehension , 2020, ArXiv.