100 citations · 183 across the 23 of their papers we have counts for
6 papers · 1 filter
Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives
Si Sun, Chenyan Xiong, Yue Yu +3
In this paper, we investigate the instability in the standard dense retrieval training, which iterates between model training and hard negative selection using the being-trained mo…
Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal Retrieval
Zhenghao Liu, Chenyan Xiong, Yuanhuiyi Lv +2
This paper presents Universal Vision-Language Dense Retrieval (UniVL-DR), which builds a unified model for multi-modal retrieval. UniVL-DR encodes queries and multi-modality resour…
Improving Task Generalization via Unified Schema Prompt
Wanjun Zhong, Yifan Gao, Ning Ding +5
Task generalization has been a long standing challenge in Natural Language Processing (NLP). Recent research attempts to improve the task generalization ability of pre-trained lang…
P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning
Xiaomeng Hu, Shi Yu, Chenyan Xiong +3
Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and st…
Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder
Zhenghao Liu, Han Zhang, Chenyan Xiong +3
Dense retrievers encode queries and documents and map them in an embedding space using pre-trained language models. These embeddings need to be high-dimensional to fit training sig…
Prototypical Verbalizer for Prompt-based Few-shot Tuning
Ganqu Cui, Shengding Hu, Ning Ding +2
Prompt-based tuning for pre-trained language models (PLMs) has shown its effectiveness in few-shot learning. Typically, prompt-based tuning wraps the input text into a cloze questi…