9 citations · 16 across the 6 of their papers we have counts for
6 papers
Towards Consistency Filtering-Free Unsupervised Learning for Dense Retrieval
Haoxiang Shi, Sumio Fujita, Tetsuya Sakai
Domain transfer is a prevalent challenge in modern neural Information Retrieval (IR). To overcome this problem, previous research has utilized domain-specific manual annotations an…
A Meta-Evaluation of C/W/L/A Metrics: System Ranking Similarity, System Ranking Consistency and Discriminative Power
Nuo Chen, Tetsuya Sakai
Recently, Moffat et al. proposed an analytic framework, namely C/W/L/A, for offline evaluation metrics. This framework allows information retrieval (IR) researchers to design evalu…
NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension
Yuxiang Zhang, Junjie Wang, Xinyu Zhu +2
Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research…
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Qijiong Liu, Nuo Chen, Tetsuya Sakai +1
Personalized content-based recommender systems have become indispensable tools for users to navigate through the vast amount of content available on platforms like daily news websi…
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective
Ping Yang, Junjie Wang, Ruyi Gan +6
We propose a new paradigm for zero-shot learners that is format agnostic, i.e., it is compatible with any format and applicable to a list of language tasks, such as text classifica…
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
Yatai Ji, Junjie Wang, Yuan Gong +6
Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our i…