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RUCIR at the NTCIR-16 Session Search (SS) Task
Get PDFThis paper presents the participation of RUCIR in the NTCIR-16 Session Search Task. We will discuss the approach we use to solve the problem and the experimental results. We use the state-of-the-art session search ranking model COCA which is based on BERT and contrastive learning. In addition, we use the BM25 algorithm and usefulness labels to make our ranking results more accurate. The official results show that our best run outperforms all other participants' runs in terms of all official metrics in both subtasks.conference pape
UHGSIS at the NTCIR-16 Data Search 2 IR Subtask: A BERT-based Query Modification Approach
Get PDFWe describe a framework using the BERT-based query modification technique for the NTCIR-16 Data Search 2 IR Subtask. In our framework, we took a 3-step procedure: (1) the query modification, (2) item filtering by BM25, and (3) item re-ranking by BERT. The experimental results showed that our framework using the query modification did not outperform the baseline method that does not use the query modification.conference pape
STIS at the NTCIR-16 Data Search 2 Task: Ad-hoc Data Retrieval Ranking with Pretrained Representative Words Prediction
Get PDFIn this paper, we present the system and results of The STIS team for the Information Retrieval (English) subtasks of the NTCIR-16 Data Search Task. The data collections in this task consist of a pair of metadata and a set of data files. We only used title, description, and tags of metadata as input documents of our proposed approach to retrieve a rank of query-related data files. We proposed using a pre-trained model to capture representative words prediction for each document then calculate the similarity between the query and the representative words as a rank score.conference pape
IMNTPU Dialogue System Evaluation at the NTCIR-16 DialEval-2 Dialogue Quality and Nugget Detection
Get PDFIn recent years, there has been a surge in interest in evaluating the quality of chatbot conversation. We participated in the Dialogue Quality (DQ) and Nugget Detection (ND) subtasks in both Chinese and English. However, the majority of existing conventional approaches are based on the long short-term memory (LSTM) model. The paper suggests a method for assisting customers in resolving problems. The goal of this subtask is to automatically determine the status of dialogue sentences in a dialogue system's logs. On conversation tasks, we developed fine-tuning methodologies for the Transformer model. To evaluate and show the concept, we created a wide framework for testing and displaying the XLM-RoBERTa model's performance on conversational texts. Finally, the experimental findings of the two subtasks demonstrate the efficacy of our strategy. The experimental findings for the DialEval-2 task show that the suggested method's performance is reasonably equal to that of an LSTM-based baseline model. The main contribution of our study is that we suggested two crucial elements for increasing conversation quality and nugget identification subtasks in dialogue assessment, namely tokenization methods and finetuning procedures.conference pape
DCU and HCMUS at NTCIR-16 Lifelog-4
Get PDFIn this paper, we present our DCU and HCMUS team’s participation in the NTCIR16 Lifelog-4 task by using two different retrieval systems, namely LifeSeeker and Myscéal that were originally introduced in the Lifelog Search Challenge (LSC) and adapted for addressing the Lifelog Semantic Access Task (LSAT). To tackle the task in an automatic manner, both LifeSeeker and Myscéal employed pre-processing techniques as part of the retrieval process, while LifeSeeker further utilised a post-processing step to refine the retrieval results. Regarding the interactive manner, we evaluated Myscéal system by conducting a user study on both expert and novice users on both ad-hoc and known-item-search settings.conference pape
Overview of the NTCIR-16 Unbiased Learning to Rank Evaluation (ULTRE) Task
Get PDFIn this paper, we present an overview of the NTCIR-16 Unbiased Learning to Rank (ULTRE) task. The ULTRE task is motivated by the ongoing development of Unbiased Learning to Rank research, consisting of two subtasks: offline ULTR and online ULTR. In the overview, we introduce the dataset , simulation method and evaluation protocols of ULTRE, and report the official evaluation results of the received runs.conference pape
SPARC Japan セミナー2021 「研究データポリシーが目指すものとは」 研究ＤＸを巡る政策動向から見る研究データポリシーの役割 発表資料
Get PDFSPARC Japan セミナー2021「研究データポリシーが目指すものとは」
開催場所：オンライン開催
日時：2022年2月22日（火）13:00-16:55conference objec
Relation Types used in the ResourceSync Framework
Get PDF本アイテムは「Relation Types used in the ResourceSync Framework」を国立情報学研究所で翻訳したものである。othe
大学図書館職員の スキルアップ法
Get PDF研修名：2022年度大学図書館職員短期研修
開催期間：2022年10月18日（火）～10月21日（金）
主催：東京大学附属図書館、京都大学附属図書館、国立情報学研究所othe