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2021 年度 実績報告書

大規模の逆強化学習ための文脈データを収集するシステムの開発

研究課題

研究課題/領域番号 17K00295
研究機関北海道大学

研究代表者

RZEPKA Rafal  北海道大学, 情報科学研究院, 助教 (80396316)

研究期間 (年度) 2017-04-01 – 2022-03-31
キーワードcorpus creation / story generation / danger detection / commonsense
研究実績の概要

During the final year of the grant period I concentrated on data creation. As there is a growing number of datasets for various languages and less for Japanese, I have used crowdsourcing for making 1) danger-safety simple sentences dataset and 2) a story dataset based on previously created simple sentences.
The first one contains over 21,000 sentences written by crowdworkers based on "dangerous" nad "safe" verbs. The entries differ in gender or age of agents and patients, which can be used for bias detection. Other group of crowdworkers evaluated how dangerous a given act is and the human evaluation can be used for testing AI’s capability for recognizing potentially dangerous situations.
The story corpus is probably the first such dataset for Japanese and it contains 8,000 five sentence stories, which can be used by researches for various tasks. I have used almost the whole sum planned to be spent for the year to hire crowdworkers with various backgrounds, genders and ages. The acquired data is based on the first dataset and can be used beyond the purpose for the grant topic (being used for discovering thought process to be used in inverse reinforcement learning): natural language processing specialists can utilize it for story understanding and generation, causal and temporal knowledge processing, etc.
I have managed to perform experiments on the first dataset (Japanese BERT language model appeared to have lower error rate than LSTM and BiLSTM) but evaluating the story generation output is problematic.

  • 研究成果

    (4件)

すべて 2022 2021

すべて 学会発表 (4件) (うち国際学会 3件)

  • [学会発表] Comparison of Zero-Shot Ethical Classification With and Without Automatically Generated Consequences2022

    • 著者名/発表者名
      Rafal Rzepka, Yuki Katsumata, Kenji Araki
    • 学会等名
      AAAI Spring Symposium on Approaches to Ethical Computing Metrics for Measuring AI’s Proficiency and Competency for Ethical Reasoning
    • 国際学会
  • [学会発表] Pitfalls of Current AI Helping To Analyze Japanese Tweets2022

    • 著者名/発表者名
      Rafal Rzepka
    • 学会等名
      Practicing Japan - 35 years of Japanese Studies in Poznan and Krakow Conference
    • 国際学会
  • [学会発表] 文脈による危険度変化の予測のためのデータセット構築2022

    • 著者名/発表者名
      勝又友輝, 竹下昌志, ジェプカラファウ, 荒木健治
    • 学会等名
      言語処理学会
  • [学会発表] Current Language Models Might Not Be Suitable For Reverse Engineering Moral Wisdom of Crowds2021

    • 著者名/発表者名
      Rafal Rzepka, Yuki Katsumata, Kenji Araki
    • 学会等名
      Engineering and Reverse-Engineering Morality Workshop at CogSci 2021
    • 国際学会

URL: 

公開日: 2022-12-28  

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