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2-Phase Passage Re-ranking Model based on Neural-Symbolic Ranking Models
Yongjin Bae, Hyun Kim, Joon-Ho Lim, Hyun-ki Kim, Kong Joo Lee
http://doi.org/10.5626/JOK.2021.48.5.501
Previous researches related to the QA system have focused on extracting exact answers for the given questions and passages. However, when expanding the problem from machine reading comprehension to open domain question answering, finding the passage containing the correct answer is as important as machine reading comprehension. DrQA reported that Exact Match@Top1 performance decreased from 69.5 to 27.1 when the QA system had the initial search step. In the present work, we have proposed the 2-phase passage reranking model to improve the performance of the question answering system. The proposed model integrates the results of the symbolic and neural ranking models to re-rank them again. The symbolic ranking model was trained based on the CatBoost algorithm and manual features between the question and passage. The neural model was trained based on the KorBERT model by fine-tuning. The second stage model was trained based on the neural regression model. We maximized the performance by combining ranking models with different characters. Finally, the proposed model showed the performance of 85.8% via MRR and 82.2% via BinaryRecall@Top1 measure while evaluating 1,000 questions. Each performance was improved by 17.3%(MRR) and 22.3%(BR@Top1) compared with the baseline model.
Passage Re-ranking Method Based on Sentence Similarity Through Multitask Learning
Youngjin Jang, Hyeon-gu Lee, Jihyun Wang, Chunghee Lee, Harksoo Kim
http://doi.org/10.5626/JOK.2020.47.4.416
The machine reading comprehension(MRC) system is a question answering system in which a computer understands a given passage and respond questions. Recently, with the development of the deep neural network, research on the machine reading system has been actively conducted, and the open domain machine reading system that identifies the correct answer from the results of the information retrieval(IR) model rather than the given passage is in progress. However, if the IR model fails to identify a passage comprising the correct answer, the MRC system cannot respond to the question. That is, the performance of the open domain MRC system depends on the performance of the IR model. Thus, for an open domain MRC system to record high performance, a high performance IR model must be preceded. The previous IR model has been studied through query expansion and reranking. In this paper, we propose a re-ranking method using deep neural networks. The proposed model re-ranks the retrieval results (passages) through multi-task learning-based sentence similarity, and improves the performance by approximately 8% compared to the performance of the existing IR model with experimental results of 58,980 pairs of MRC data.
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