198 citations · 1.3k across the 62 of their papers we have counts for
62 papers
Elastic Information Bottleneck
Yuyan Ni, Yanyan Lan, Ao Liu +1
Information bottleneck is an information-theoretic principle of representation learning that aims to learn a maximally compressed representation that preserves as much information…
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Qingyao Ai, Ting Bai, Zhao Cao +30
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
Bo Qiang, Yuxuan Song, Minkai Xu +5
Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate m…
Visual Reasoning: from State to Transformation
Xin Hong, Yanyan Lan, Liang Pang +2
Most existing visual reasoning tasks, such as CLEVR in VQA, ignore an important factor, i.e.~transformation. They are solely defined to test how well machines understand concepts a…
Visual Transformation Telling
Wanqing Cui, Xin Hong, Yanyan Lan +3
Humans can naturally reason from superficial state differences (e.g. ground wetness) to transformations descriptions (e.g. raining) according to their life experience. In this pape…
Multi-video Moment Ranking with Multimodal Clue
Danyang Hou, Liang Pang, Yanyan Lan +2
Video corpus moment retrieval~(VCMR) is the task of retrieving a relevant video moment from a large corpus of untrimmed videos via a natural language query. State-of-the-art work f…
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding
Yunchang Zhu, Liang Pang, Kangxi Wu +3
Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. Whi…
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction
Yuancheng Sun, Yimeng Chen, Weizhi Ma +5
Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances.…
When Does Group Invariant Learning Survive Spurious Correlations?
Yimeng Chen, Ruibin Xiong, Zhiming Ma +1
By inferring latent groups in the training data, recent works introduce invariant learning to the case where environment annotations are unavailable. Typically, learning group inva…
LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback
Yunchang Zhu, Liang Pang, Yanyan Lan +2
Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressio…
Uncertainty Calibration for Ensemble-Based Debiasing Methods
Ruibin Xiong, Yimeng Chen, Liang Pang +2
Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a bias-only model to adju…
Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types
Shentong Mo, Xi Fu, Chenyang Hong +6
In the genome biology research, regulatory genome modeling is an important topic for many regulatory downstream tasks, such as promoter classification, transaction factor binding s…
Adaptive Bridge between Training and Inference for Dialogue
Haoran Xu, Hainan Zhang, Yanyan Zou +3
Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scena…
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning
Xu Wang, Hainan Zhang, Shuai Zhao +5
Despite the success of neural dialogue systems in achieving high performance on the leader-board, they cannot meet users' requirements in practice, due to their poor reasoning skil…
Transductive Learning for Unsupervised Text Style Transfer
Fei Xiao, Liang Pang, Yanyan Lan +3
Unsupervised style transfer models are mainly based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and…
Adaptive Information Seeking for Open-Domain Question Answering
Yunchang Zhu, Liang Pang, Yanyan Lan +2
Information seeking is an essential step for open-domain question answering to efficiently gather evidence from a large corpus. Recently, iterative approaches have been proven to b…
Toward the Understanding of Deep Text Matching Models for Information Retrieval
Lijuan Chen, Yanyan Lan, Liang Pang +2
Semantic text matching is a critical problem in information retrieval. Recently, deep learning techniques have been widely used in this area and obtained significant performance im…
A Discriminative Semantic Ranker for Question Retrieval
Yinqiong Cai, Yixing Fan, Jiafeng Guo +3
Similar question retrieval is a core task in community-based question answering (CQA) services. To balance the effectiveness and efficiency, the question retrieval system is typica…
Pre-Trained Models: Past, Present and Future
Xu Han, Zhengyan Zhang, Ning Ding +21
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophis…
Sketch and Customize: A Counterfactual Story Generator
Changying Hao, Liang Pang, Yanyan Lan +3
Recent text generation models are easy to generate relevant and fluent text for the given text, while lack of causal reasoning ability when we change some parts of the given text.…
Probing Product Description Generation via Posterior Distillation
Haolan Zhan, Hainan Zhang, Hongshen Chen +5
In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks.…
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
Yuqi Huo, Manli Zhang, Guangzhen Liu +32
Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction bet…
A Linguistic Study on Relevance Modeling in Information Retrieval
Yixing Fan, Jiafeng Guo, Xinyu Ma +3
Relevance plays a central role in information retrieval (IR), which has received extensive studies starting from the 20th century. The definition and the modeling of relevance has…
Learning to Truncate Ranked Lists for Information Retrieval
Chen Wu, Ruqing Zhang, Jiafeng Guo +3
Ranked list truncation is of critical importance in a variety of professional information retrieval applications such as patent search or legal search. The goal is to dynamically d…