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SPARC Japan セミナー2023 「即時OAに備えて：論文・データを「つかってもらう」ためのライセンス再入門」 前半質疑応答 ドキュメント
Get PDFSPARC Japan セミナー2023「即時OAに備えて：論文・データを「つかってもらう」ためのライセンス再入門」
開催場所：オンライン開催
日時：2023年11月28日（火）13:00～17:00conference objec
ISLab at the NTCIR-17 QA Lab-PoliInfo-4: Models for Automatically Identifying Politicians' Stances on Bills
Get PDFThis paper aims to design a model that can determine whether the politician's stance is approved or disapproved the bill based on the politician's utterance on a specific bill in the parliament. This study proposed two frameworks for determining the stance in utterances. The first framework involves concatenating BERT model with Bi-LSTM model to form a comprehensive decision-making model while the second framework is concatenating Curie model with ChatGPT model. This paper used the dataset provided by Stance Classification 2 task in NTCIR-17 for model training and testing, and GPT-based model this paper proposed achieved an accuracy of 0.932.conference pape
RSLFW at the NTCIR-17 FairWeb-1 Task
Get PDFThe RSLFW team participated in the NTCIR-17 FairWeb 1 Task. This paper reports our approach to solving the problem and dis- cusses the official results. We applied several different methods to generate 5 runs, including PM-1, PM-2 and DetGreedy algorithm, all of which are post-processing approaches. We also utilized COIL (Contextualize Inverted List) as the RSLFW baseline. By combining official baseline and COIL baseline with different fairness-related algorithm, we analyzed the results of those methods. Our reranked run outperforms the baseline, resulting in an improved GFR score.conference pape
HUKB at the NTCIR-17 QA Lab-PoliInfo-4 Task
Get PDFThe HUKB team participated in the Question Answering-2 subtask in the NTCIR-17 QA Lab-PoliInfo-4 task. Our proposed method is divided into three steps. First, we found the sentence on the beginning of the same topic as the input question from the respondent’s utterances and extracted the candidate sentences. Next, we found the sentences where the respondent seemed to answer the input question directly, using BERT. Finally, we entered the selected sentences with the input question into the T5 based summarizer, and generated the answer summary. We evaluated the whole method and each process with the dataset distributed by Task Organizers.conference pape
THUIR at the NTCIR-17 FairWeb-1 Task: An Initial Exploration of the Relationship Between Relevance and Fairness
Get PDFThe fairness of search systems has become an important research topic to the IR community. This paper presents and discusses the efforts of the THUIR team in developing effective and fair retrieval models and ranking algorithms in the NTCIR-17 FairWeb-1 Task. Specifically, we utilize several different methods in all 5 submitted runs including reranking, learning-to-rank, and search result diversification algorithms to deal with the group fairness problem in web search. The final report of the FairWeb-1 Task indicates that our methods have outperformed other competitors on both result relevance and fairness. In terms of the GFR (Group Fairness Relevance) metric, our methods respectively outperform the second-ranked team by 9.74%, 17.8%, and 19.8% on three topics of queries.conference pape