


default search action
Tetsuya Sakai
Person information
- affiliation: Waseda University, Japan
Refine list

refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j55]Junjie Wang
, Yatai Ji, Yuxiang Zhang
, Yanru Zhu, Tetsuya Sakai
:
Modeling Multimodal Uncertainties via Probability Distribution Encoders Included Vision-Language Models. IEEE Access 12: 420-434 (2024) - [j54]Haoxiang Shi
, Tetsuya Sakai
:
Enhancing Parameter Efficiency in Model Inference Using an Ultralight Inter-Transformer Linear Structure. IEEE Access 12: 43734-43746 (2024) - [j53]Tetsuya Sakai
, Jinyoung Kim
, Inho Kang
:
A Versatile Framework for Evaluating Ranked Lists in Terms of Group Fairness and Relevance. ACM Trans. Inf. Syst. 42(1): 11:1-11:36 (2024) - [j52]Kevin Roitero
, David La Barbera
, Michael Soprano
, Gianluca Demartini
, Stefano Mizzaro
, Tetsuya Sakai
:
How Many Crowd Workers Do I Need? On Statistical Power when Crowdsourcing Relevance Judgments. ACM Trans. Inf. Syst. 42(1): 21:1-21:26 (2024) - [j51]Tetsuya Sakai
, Sijie Tao
, Nuo Chen
, Yujing Li
, Maria Maistro
, Zhumin Chu
, Nicola Ferro
:
On the Ordering of Pooled Web Pages, Gold Assessments, and Bronze Assessments. ACM Trans. Inf. Syst. 42(1): 23:1-23:31 (2024) - [j50]Yu-Xiang Zhang
, Junjie Wang
, Xinyu Zhu
, Tetsuya Sakai
, Hayato Yamana
:
SSR: Solving Named Entity Recognition Problems via a Single-stream Reasoner. ACM Trans. Inf. Syst. 42(5): 138:1-138:28 (2024) - [c211]Kelong Mao, Chenlong Deng, Haonan Chen, Fengran Mo, Zheng Liu, Tetsuya Sakai, Zhicheng Dou:
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval. EMNLP 2024: 1227-1240 - [c210]Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana:
ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models. EMNLP 2024: 11388-11422 - [c209]Tetsuya Sakai:
Evaluating System Responses Based On Overconfidence and Underconfidence. EMTCIR/UM-CIR@SIGIR-AP 2024 - [c208]Nuo Chen
, Jiqun Liu
, Xiaoyu Dong
, Qijiong Liu
, Tetsuya Sakai
, Xiao-Ming Wu
:
AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment. SIGIR-AP 2024: 54-63 - [c207]Yuxiang Zhang
, Xin Fan
, Junjie Wang
, Chongxian Chen
, Fan Mo
, Tetsuya Sakai
, Hayato Yamana
:
Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models. SIGIR-AP 2024: 226-235 - [c206]Qijiong Liu
, Nuo Chen
, Tetsuya Sakai
, Xiao-Ming Wu
:
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models. WSDM 2024: 452-461 - [e14]Tetsuya Sakai, Emi Ishita, Hiroaki Ohshima, Faegheh Hasibi, Jiaxin Mao, Joemon M. Jose:
Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, SIGIR-AP 2024, Tokyo, Japan, December 9-12, 2024. ACM 2024, ISBN 979-8-4007-0724-7 [contents] - [i30]Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, Xiao-Ming Wu:
Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability. CoRR abs/2403.18462 (2024) - [i29]Kelong Mao, Chenlong Deng
, Haonan Chen, Fengran Mo, Zheng Liu, Tetsuya Sakai, Zhicheng Dou:
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval. CoRR abs/2404.13556 (2024) - [i28]Qijiong Liu, Xiaoyu Dong, Jiaren Xiao, Nuo Chen, Hengchang Hu, Jieming Zhu, Chenxu Zhu, Tetsuya Sakai, Xiao-Ming Wu:
Vector Quantization for Recommender Systems: A Review and Outlook. CoRR abs/2405.03110 (2024) - [i27]Haoxiang Shi, Jiaan Wang, Jiarong Xu, Cen Wang, Tetsuya Sakai:
CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Models. CoRR abs/2405.12174 (2024) - [i26]Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana:
ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models. CoRR abs/2406.20015 (2024) - [i25]Nuo Chen, Jiqun Liu, Xiaoyu Dong, Qijiong Liu, Tetsuya Sakai, Xiao-Ming Wu:
AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment. CoRR abs/2409.16022 (2024) - [i24]Yuxiang Zhang, Xin Fan, Junjie Wang, Chongxian Chen, Fan Mo, Tetsuya Sakai, Hayato Yamana:
Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models. CoRR abs/2410.03212 (2024) - [i23]Yiruo Cheng, Kelong Mao, Ziliang Zhao, Guanting Dong, Hongjin Qian, Yongkang Wu, Tetsuya Sakai, Ji-Rong Wen, Zhicheng Dou:
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation. CoRR abs/2410.23090 (2024) - [i22]Rikiya Takehi, Ellen M. Voorhees, Tetsuya Sakai:
LLM-Assisted Relevance Assessments: When Should We Ask LLMs for Help? CoRR abs/2411.06877 (2024) - 2023
- [j49]Haoxiang Shi
, Tetsuya Sakai:
Self-Supervised and Few-Shot Contrastive Learning Frameworks for Text Clustering. IEEE Access 11: 84134-84143 (2023) - [j48]Junjie Wang
, Ping Yang, Ruyi Gan, Yuxiang Zhang
, Jiaxing Zhang, Tetsuya Sakai
:
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple-Choice Perspective. IEEE Access 11: 142829-142845 (2023) - [j47]Tetsuya Sakai:
On a Few Responsibilities of (IR) Researchers (Fairness, Awareness, and Sustainability): A Keynote at ECIR 2023. SIGIR Forum 57(1): 4:1-4:7 (2023) - [j46]Tetsuya Sakai:
Evaluating Parrots and Sociopathic Liars: A keynote at ICTIR 2023. SIGIR Forum 57(2): 3:1-3:7 (2023) - [c205]Yatai Ji, Junjie Wang, Yuan Gong, Lin Zhang, Yanru Zhu, Hongfa Wang, Jiaxing Zhang, Tetsuya Sakai, Yujiu Yang
:
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model. CVPR 2023: 23262-23271 - [c204]Tetsuya Sakai
:
Evaluating Parrots and Sociopathic Liars (keynote). ICTIR 2023: 1 - [c203]Nuo Chen
, Donghyun Park
, Hyungae Park
, Kijun Choi
, Tetsuya Sakai
, Jinyoung Kim
:
Practice and Challenges in Building a Business-oriented Search Engine Quality Metric. SIGIR 2023: 3295-3299 - [c202]Yiyao Yu
, Junjie Wang
, Yuxiang Zhang
, Lin Zhang
, Yujiu Yang
, Tetsuya Sakai
:
EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval. SIGIR-AP 2023: 32-39 - [c201]Rikiya Takehi
, Akihisa Watanabe
, Tetsuya Sakai
:
Open-Domain Dialogue Quality Evaluation: Deriving Nugget-level Scores from Turn-level Scores. SIGIR-AP 2023: 40-45 - [c200]Zhumin Chu
, Tetsuya Sakai
, Qingyao Ai
, Yiqun Liu
:
Chuweb21D: A Deduped English Document Collection for Web Search Tasks. SIGIR-AP 2023: 63-72 - [c199]Reo Yoshikoshi, Tetsuya Sakai:
RSLTOT at the TREC 2023 ToT Track. TREC 2023 - [c198]Nuo Chen
, Jiqun Liu
, Tetsuya Sakai
:
A Reference-Dependent Model for Web Search Evaluation: Understanding and Measuring the Experience of Boundedly Rational Users. WWW 2023: 3396-3405 - [e13]Qingyao Ai, Yiqin Liu, Alistair Moffat, Xuanjing Huang, Tetsuya Sakai, Justin Zobel:
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, SIGIR-AP 2023, Beijing, China, November 26-28, 2023. ACM 2023 [contents] - [i21]Yuxiang Zhang, Junjie Wang, Xinyu Zhu, Tetsuya Sakai, Hayato Yamana:
NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension. CoRR abs/2305.03970 (2023) - [i20]Qijiong Liu, Nuo Chen, Tetsuya Sakai, Xiao-Ming Wu:
A First Look at LLM-Powered Generative News Recommendation. CoRR abs/2305.06566 (2023) - [i19]Tetsuya Sakai:
SWAN: A Generic Framework for Auditing Textual Conversational Systems. CoRR abs/2305.08290 (2023) - [i18]Nuo Chen, Tetsuya Sakai:
A Meta-Evaluation of C/W/L/A Metrics: System Ranking Similarity, System Ranking Consistency and Discriminative Power. CoRR abs/2307.02936 (2023) - [i17]Haoxiang Shi, Sumio Fujita, Tetsuya Sakai:
Towards Consistency Filtering-Free Unsupervised Learning for Dense Retrieval. CoRR abs/2308.02926 (2023) - [i16]Rikiya Takehi, Akihisa Watanabe, Tetsuya Sakai:
Open-Domain Dialogue Quality Evaluation: Deriving Nugget-level Scores from Turn-level Scores. CoRR abs/2310.00410 (2023) - [i15]Yiyao Yu, Junjie Wang, Yuxiang Zhang, Lin Zhang, Yujiu Yang, Tetsuya Sakai:
EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval. CoRR abs/2310.00970 (2023) - [i14]Nuo Chen, Jiqun Liu, Tetsuya Sakai, Xiao-Ming Wu:
Decoy Effect in Search Interaction: A Pilot Study. CoRR abs/2311.02362 (2023) - 2022
- [j45]Tetsuya Sakai
, Sijie Tao
, Zhaohao Zeng
:
Relevance Assessments for Web Search Evaluation: Should We Randomise or Prioritise the Pooled Documents? ACM Trans. Inf. Syst. 40(4): 76:1-76:35 (2022) - [c197]Haoxiang Shi, Rongsheng Zhang, Jiaan Wang, Cen Wang, Yinhe Zheng, Tetsuya Sakai:
LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency. COLING 2022: 3120-3126 - [c196]Riku Togashi
, Mayu Otani, Yuta Nakashima, Esa Rahtu
, Janne Heikkilä, Tetsuya Sakai:
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval. CVPR 2022: 21044-21053 - [c195]Ping Yang, Junjie Wang, Ruyi Gan, Xinyu Zhu, Lin Zhang, Ziwei Wu, Xinyu Gao, Jiaxing Zhang, Tetsuya Sakai:
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective. EMNLP 2022: 7042-7055 - [c194]Rei Shimizu, Sumio Fujita, Tetsuya Sakai:
Do Extractive Summarization Algorithms Amplify Lexical Bias in News Articles? ICTIR 2022: 133-137 - [c193]Yuji Naraki, Tetsuya Sakai, Yoshihiko Hayashi:
Evaluating the Effects of Embedding with Speaker Identity Information in Dialogue Summarization. LREC 2022: 298-304 - [c192]Atsuko Natatsuka, Ryo Iijima, Takuya Watanabe, Mitsuaki Akiyama, Tetsuya Sakai, Tatsuya Mori:
Understanding the Behavior Transparency of Voice Assistant Applications Using the ChatterBox Framework. RAID 2022: 143-159 - [c191]Nuo Chen
, Fan Zhang
, Tetsuya Sakai:
Constructing Better Evaluation Metrics by Incorporating the Anchoring Effect into the User Model. SIGIR 2022: 2709-2714 - [i13]Riku Togashi, Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Tetsuya Sakai:
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval. CoRR abs/2203.16062 (2022) - [i12]Tetsuya Sakai, Jin Young Kim, Inho Kang:
A Versatile Framework for Evaluating Ranked Lists in terms of Group Fairness and Relevance. CoRR abs/2204.00280 (2022) - [i11]Tetsuya Sakai:
On Variants of Root Normalised Order-aware Divergence and a Divergence based on Kendall's Tau. CoRR abs/2204.07304 (2022) - [i10]Yatai Ji, Junjie Wang, Yuan Gong, Lin Zhang, Yanru Zhu, Hongfa Wang, Jiaxing Zhang, Tetsuya Sakai, Yujiu Yang:
MAP: Modality-Agnostic Uncertainty-Aware Vision-Language Pre-training Model. CoRR abs/2210.05335 (2022) - [i9]Ping Yang, Junjie Wang, Ruyi Gan, Xinyu Zhu, Lin Zhang, Ziwei Wu, Xinyu Gao, Jiaxing Zhang, Tetsuya Sakai:
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective. CoRR abs/2210.08590 (2022) - [i8]Tetsuya Sakai, Sijie Tao, Maria Maistro, Zhumin Chu, Yujing Li, Nuo Chen, Nicola Ferro, Junjie Wang, Ian Soboroff, Yiqun Liu:
Corrected Evaluation Results of the NTCIR WWW-2, WWW-3, and WWW-4 English Subtasks. CoRR abs/2210.10266 (2022) - [i7]Tetsuya Sakai, Sijie Tao, Zhaohao Zeng:
Relevance Assessments for Web Search Evaluation: Should We Randomise or Prioritise the Pooled Documents? (Corrected Version). CoRR abs/2211.00981 (2022) - 2021
- [j44]Tetsuya Sakai, Zhaohao Zeng:
Retrieval Evaluation Measures that Agree with Users' SERP Preferences: Traditional, Preference-based, and Diversity Measures. ACM Trans. Inf. Syst. 39(2): 14:1-14:35 (2021) - [c190]Tetsuya Sakai:
Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification. ACL/IJCNLP (1) 2021: 2759-2769 - [c189]Jia Chen
, Yiqun Liu, Jiaxin Mao, Fan Zhang
, Tetsuya Sakai, Weizhi Ma, Min Zhang, Shaoping Ma:
Incorporating Query Reformulating Behavior into Web Search Evaluation. CIKM 2021: 171-180 - [c188]Zhumin Chu, Jiaxin Mao, Fan Zhang
, Yiqun Liu, Tetsuya Sakai, Min Zhang, Shaoping Ma:
Evaluating Relevance Judgments with Pairwise Discriminative Power. CIKM 2021: 261-270 - [c187]Tetsuya Sakai:
A Closer Look at Evaluation Measures for Ordinal Quantification. CIKM Workshops 2021 - [c186]Yuki Amemiya, Tomohiro Manabe, Sumio Fujita, Tetsuya Sakai:
How Do Users Revise Zero-Hit Product Search Queries? ECIR (2) 2021: 185-192 - [c185]Tetsuya Sakai:
On the Instability of Diminishing Return IR Measures. ECIR (1) 2021: 572-586 - [c184]Junjie Wang, Yatai Ji, Jiaqi Sun, Yujiu Yang
, Tetsuya Sakai:
MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question Answering. EMNLP (Findings) 2021: 2280-2292 - [c183]Rikiya Suzuki, Tetsuya Sakai:
A Fast and Exact Randomisation Test for Comparing Two Systems with Paired Data. ICTIR 2021: 239-243 - [c182]Riku Togashi
, Masahiro Kato, Mayu Otani, Tetsuya Sakai, Shin'ichi Satoh:
Scalable Personalised Item Ranking through Parametric Density Estimation. SIGIR 2021: 921-931 - [c181]Tetsuya Sakai:
On the Two-Sample Randomisation Test for IR Evaluation. SIGIR 2021: 1980-1984 - [c180]Tetsuya Sakai, Sijie Tao, Zhaohao Zeng:
WWW3E8: 259, 000 Relevance Labels for Studying the Effect of Document Presentation Order for Relevance Assessors. SIGIR 2021: 2376-2382 - [c179]Haoxiang Shi, Cen Wang, Tetsuya Sakai:
A Simple and Effective Usage of Self-supervised Contrastive Learning for Text Clustering. SMC 2021: 315-320 - [e12]Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, Tetsuya Sakai:
SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021. ACM 2021, ISBN 978-1-4503-8037-9 [contents] - [i6]Zhaohao Zeng, Tetsuya Sakai:
DCH-2: A Parallel Customer-Helpdesk Dialogue Corpus with Distributions of Annotators' Labels. CoRR abs/2104.08755 (2021) - [i5]Riku Togashi, Masahiro Kato, Mayu Otani, Tetsuya Sakai, Shin'ichi Satoh:
Scalable Personalised Item Ranking through Parametric Density Estimation. CoRR abs/2105.04769 (2021) - 2020
- [j43]Zhicheng Dou
, Xue Yang, Diya Li, Ji-Rong Wen, Tetsuya Sakai:
Low-cost, bottom-up measures for evaluating search result diversification. Inf. Retr. J. 23(1): 86-113 (2020) - [j42]Tetsuya Sakai:
On Fuhr's guideline for IR evaluation. SIGIR Forum 54(1): 12:1-12:8 (2020) - [c178]Haoxiang Shi, Cen Wang, Tetsuya Sakai:
A Siamese CNN Architecture for Learning Chinese Sentence Similarity. AACL/IJCNLP (Student Research Workshop) 2020: 24-29 - [c177]Riku Togashi, Sumio Fujita, Tetsuya Sakai:
Automatic Evaluation of Iconic Image Retrieval based on Colour, Shape, and Texture. ICMR 2020: 346-354 - [c176]Timo Breuer, Nicola Ferro, Norbert Fuhr, Maria Maistro
, Tetsuya Sakai, Philipp Schaer, Ian Soboroff:
How to Measure the Reproducibility of System-oriented IR Experiments. SIGIR 2020: 349-358 - [c175]Tetsuya Sakai, Zhaohao Zeng:
Good Evaluation Measures based on Document Preferences. SIGIR 2020: 359-368 - [c174]Riku Togashi
, Tetsuya Sakai:
Visual Intents vs. Clicks, Likes, and Purchases in E-commerce. SIGIR 2020: 1869-1872 - [c173]Sijie Tao, Tetsuya Sakai:
RealSakaiLab at the TREC 2020 Health Misinformation Track. TREC 2020 - [i4]Shiyoh Goetsu, Tetsuya Sakai:
Different Types of Voice User Interface Failures May Cause Different Degrees of Frustration. CoRR abs/2002.03582 (2020) - [i3]Timo Breuer, Nicola Ferro, Norbert Fuhr, Maria Maistro, Tetsuya Sakai, Philipp Schaer, Ian Soboroff:
How to Measure the Reproducibility of System-oriented IR Experiments. CoRR abs/2010.13447 (2020)
2010 – 2019
- 2019
- [j41]Zhaohao Zeng, Ruihua Song, Pingping Lin, Tetsuya Sakai:
Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs. J. Inf. Process. 27: 742-751 (2019) - [j40]Guoshuai Zhao
, Hao Fu, Ruihua Song, Tetsuya Sakai, Zhongxia Chen, Xing Xie
, Xueming Qian:
Personalized Reason Generation for Explainable Song Recommendation. ACM Trans. Intell. Syst. Technol. 10(4): 41:1-41:21 (2019) - [c172]Hsin Wen Liu, Sumio Fujita, Tetsuya Sakai:
Towards Automatic Evaluation of Reused Answers in Community Question Answering. AIRS 2019: 3-9 - [c171]Sosuke Kato, Toru Shimizu, Sumio Fujita, Tetsuya Sakai:
Unsupervised Answer Retrieval with Data Fusion for Community Question Answering. AIRS 2019: 10-21 - [c170]Rikiya Suzuki, Sumio Fujita, Tetsuya Sakai:
Arc Loss: Softmax with Additive Angular Margin for Answer Retrieval. AIRS 2019: 34-40 - [c169]Tetsuya Sakai, Peng Xiao:
Randomised vs. Prioritised Pools for Relevance Assessments: Sample Size Considerations. AIRS 2019: 94-105 - [c168]Peng Xiao, Joo-Young Lee, Sijie Tao, Young-Sook Hwang, Tetsuya Sakai:
Generating Short Product Descriptors Based on Very Little Training Data. AIRS 2019: 133-144 - [c167]Atsuko Natatsuka, Ryo Iijima, Takuya Watanabe, Mitsuaki Akiyama, Tetsuya Sakai, Tatsuya Mori
:
Poster: A First Look at the Privacy Risks of Voice Assistant Apps. CCS 2019: 2633-2635 - [c166]Nicola Ferro, Norbert Fuhr, Maria Maistro, Tetsuya Sakai, Ian Soboroff:
CENTRE@CLEF2019: Overview of the Replicability and Reproducibility Tasks. CLEF (Working Notes) 2019 - [c165]Nicola Ferro, Norbert Fuhr, Maria Maistro
, Tetsuya Sakai, Ian Soboroff:
Overview of CENTRE@CLEF 2019: Sequel in the Systematic Reproducibility Realm. CLEF 2019: 287-300 - [c164]Nicola Ferro, Norbert Fuhr, Maria Maistro, Tetsuya Sakai, Ian Soboroff:
CENTRE@CLEF 2019. ECIR (2) 2019: 283-290 - [c163]Douglas W. Oard, Tetsuya Sakai, Noriko Kando:
Celebrating 20 Years of NTCIR: The Book. EVIA@NTCIR 2019 - [c162]Riku Togashi
, Tetsuya Sakai:
Generalising Kendall's Tau for Noisy and Incomplete Preference Judgements. ICTIR 2019: 193-196 - [c161]Chih-Hao Wang, Sosuke Kato, Tetsuya Sakai:
RSL19BD at DBDC4: Ensemble of Decision Tree-Based and LSTM-Based Models. IWSDS 2019: 429-441 - [c160]Chao-Chung Wu, Ruihua Song
, Tetsuya Sakai, Wen-Feng Cheng, Xing Xie
, Shou-De Lin:
Evaluating Image-Inspired Poetry Generation. NLPCC (1) 2019: 539-551 - [c159]Ryan Clancy, Nicola Ferro, Claudia Hauff, Jimmy Lin, Tetsuya Sakai, Ze Zhong Wu:
Overview of the 2019 Open-Source IR Replicability Challenge (OSIRRC 2019). OSIRRC@SIGIR 2019: 1-7 - [c158]Zhaohao Zeng, Tetsuya Sakai:
BM25 Pseudo Relevance Feedback Using Anserini at Waseda University. OSIRRC@SIGIR 2019: 62-63 - [c157]Tetsuya Sakai, Zhaohao Zeng:
Which Diversity Evaluation Measures Are "Good"? SIGIR 2019: 595-604 - [c156]Ryan Clancy, Nicola Ferro, Claudia Hauff, Jimmy Lin, Tetsuya Sakai, Ze Zhong Wu:
The SIGIR 2019 Open-Source IR Replicability Challenge (OSIRRC 2019). SIGIR 2019: 1432-1434 - [c155]Shigeichi Hirasawa, Gendo Kumoi, Hideki Yagi, Manabu Kobayashi, Masayuki Goto
, Tetsuya Sakai, Hiroshige Inazumi:
System Evaluation of Ternary Error-Correcting Output Codes for Multiclass Classification Problems. SMC 2019: 2893-2898 - [c154]Shiyoh Goetsu, Tetsuya Sakai:
Voice Input Interface Failures and Frustration: Developer and User Perspectives. UIST (Adjunct Volume) 2019: 24-26 - [c153]Zhaohao Zeng, Ruihua Song, Pingping Lin, Tetsuya Sakai:
Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs. WSDM 2019: 285-293 - [c152]Tetsuya Sakai:
Conducting Laboratory Experiments Properly with Statistical Tools: An Easy Hands-On Tutorial. WSDM 2019: 830-831 - [p1]Tetsuya Sakai:
How to Run an Evaluation Task - With a Primary Focus on Ad Hoc Information Retrieval. Information Retrieval Evaluation in a Changing World 2019: 71-102 - [e11]Ryan Clancy, Nicola Ferro, Claudia Hauff, Jimmy Lin, Tetsuya Sakai, Ze Zhong Wu:
Proceedings of the Open-Source IR Replicability Challenge co-located with 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, OSIRRC@SIGIR 2019, Paris, France, July 25, 2019. CEUR Workshop Proceedings 2409, CEUR-WS.org 2019 [contents] - [i2]Tetsuya Sakai:
Graded Relevance Assessments and Graded Relevance Measures of NTCIR: A Survey of the First Twenty Years. CoRR abs/1903.11272 (2019) - [i1]Chih-Hao Wang, Sosuke Kato, Tetsuya Sakai:
RSL19BD at DBDC4: Ensemble of Decision Tree-based and LSTM-based Models. CoRR abs/1905.01799 (2019) - 2018
- [b1]Tetsuya Sakai:
Laboratory Experiments in Information Retrieval - Sample Sizes, Effect Sizes, and Statistical Power. The Information Retrieval Series 40, Springer 2018, ISBN 978-981-13-1198-7, pp. 1-148 - [j39]Takuya Watanabe, Mitsuaki Akiyama, Tetsuya Sakai, Hironori Washizaki, Tatsuya Mori