| 講演名 | 2022-07-09 Retrieval of Similar Questions from QAbot Data based on Transformer Language Model 陳 梓豪(近畿大), 半田 久志(近畿大), 白浜 公章(近畿大), |
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| 抄録(和) | It has recently become possible to collect a large amount of question-answer pairs that arose in certain educational courses. However, one of the biggest difficulties to utilize such data is the lack of a retrieval system that helps a student efficiently find answers to his/her own question. This paper presents our system that takes advantage of a transformer language model which keeps breaking the state-of-the-art in various natural language processing tasks. In particular, our system uses Sentence-BERT to extract a useful feature of a question, so that questions semantically similar to the user’s one can be accurately identified through their feature matching. In addition, an important prerequisite for objective performance evaluation is a large-scale dataset containing many question-answer pairs. For this, a bot is developed on Slack to collect 1142 question-answer pairs that are individually annotated with the relevance to each of 20 queries. The experimental results on this dataset validate the effectiveness of our system based on Sentence-BERT. |
| 抄録(英) | It has recently become possible to collect a large amount of question-answer pairs that arose in certain educational courses. However, one of the biggest difficulties to utilize such data is the lack of a retrieval system that helps a student efficiently find answers to his/her own question. This paper presents our system that takes advantage of a transformer language model which keeps breaking the state-of-the-art in various natural language processing tasks. In particular, our system uses Sentence-BERT to extract a useful feature of a question, so that questions semantically similar to the user’s one can be accurately identified through their feature matching. In addition, an important prerequisite for objective performance evaluation is a large-scale dataset containing many question-answer pairs. For this, a bot is developed on Slack to collect 1142 question-answer pairs that are individually annotated with the relevance to each of 20 queries. The experimental results on this dataset validate the effectiveness of our system based on Sentence-BERT. |
| キーワード(和) | Question answering / Information retrieval / Transformer / Sentence-BERT / QAbot |
| キーワード(英) | Question answering / Information retrieval / Transformer / Sentence-BERT / QAbot |
| 資料番号 | ET2022-9 |
| 発行日 | 2022-07-02 (ET) |
| 研究会情報 | |
| 研究会 | ET |
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| 開催期間 | 2022/7/9(から1日開催) |
| 開催地（和） | オンライン開催 |
| 開催地（英） | Online |
| テーマ（和） | 学習履歴データの蓄積・分析と実践応用／一般 |
| テーマ（英） | Data Accumulation, Analysis and Practice Application of Learning History Data, etc. |
| 委員長氏名（和） | 渡辺 健次(広島大) |
| 委員長氏名（英） | Kenji Watanabe(Hiroshimai Univ.) |
| 副委員長氏名（和） | 國宗 永佳(千葉工大) |
| 副委員長氏名（英） | Hisayoshi Kunimune(Chiba Inst. of Tech.) |
| 幹事氏名（和） | 西尾 典洋(目白大) / 立岩 佑一郎(名工大) |
| 幹事氏名（英） | Norihiro Nisio(Mejiro Univ.) / Yuichiro Tateiwa(Nagoya Institute of Technology.) |
| 幹事補佐氏名（和） | 吉原 和明(近畿大) / 加納 徹(東京理科大) |
| 幹事補佐氏名（英） | Kazuaki Yoshihara(Kinki Univ.) / Toru Kano(Tokyo University of Science) |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Educational Technology |
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| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Retrieval of Similar Questions from QAbot Data based on Transformer Language Model |
| サブタイトル（和） | |
| キーワード(1)（和/英） | Question answering / Question answering |
| キーワード(2)（和/英） | Information retrieval / Information retrieval |
| キーワード(3)（和/英） | Transformer / Transformer |
| キーワード(4)（和/英） | Sentence-BERT / Sentence-BERT |
| キーワード(5)（和/英） | QAbot / QAbot |
| 第 1 著者 氏名（和/英） | 陳 梓豪 / Zihao Chen |
| 第 1 著者 所属（和/英） | 近畿大学(略称：近畿大) Kindai University(略称：Kindai Univ.) |
| 第 2 著者 氏名（和/英） | 半田 久志 / Hisashi Handa |
| 第 2 著者 所属（和/英） | 近畿大学(略称：近畿大) Kindai University(略称：Kindai Univ.) |
| 第 3 著者 氏名（和/英） | 白浜 公章 / Kimiaki Shirahama |
| 第 3 著者 所属（和/英） | 近畿大学(略称：近畿大) Kindai University(略称：Kindai Univ.) |
| 発表年月日 | 2022-07-09 |
| 資料番号 | ET2022-9 |
| 巻番号（vol） | vol.122 |
| 号番号（no） | ET-102 |
| ページ範囲 | pp.5-11(ET), |
| ページ数 | 7 |
| 発行日 | 2022-07-02 (ET) |