44 citations · 110 across the 13 of their papers we have counts for
13 papers
Searching Personal Collections
Michael Bendersky, Donald Metzler, Marc Najork +1
This article describes the history of information retrieval on personal document collections.
Creator Context for Tweet Recommendation
Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang +6
When discussing a tweet, people usually not only refer to the content it delivers, but also to the person behind the tweet. In other words, grounding the interpretation of the twee…
Exploring the Viability of Synthetic Query Generation for Relevance Prediction
Aditi Chaudhary, Karthik Raman, Krishna Srinivasan +3
Query-document relevance prediction is a critical problem in Information Retrieval systems. This problem has increasingly been tackled using (pretrained) transformer-based models w…
Do Not Blindly Imitate the Teacher: Using Perturbed Loss for Knowledge Distillation
Rongzhi Zhang, Jiaming Shen, Tianqi Liu +4
Knowledge distillation is a popular technique to transfer knowledge from large teacher models to a small student model. Typically, the student learns to imitate the teacher by mini…
"Why is this misleading?": Detecting News Headline Hallucinations with Explanations
Jiaming Shen, Jialu Liu, Dan Finnie +3
Automatic headline generation enables users to comprehend ongoing news events promptly and has recently become an important task in web mining and natural language processing. With…
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
Yunan Zhang, Le Yan, Zhen Qin +5
Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from implicit user feedback data such as clicks, and has been receiving considerable attention rec…
DSI++: Updating Transformer Memory with New Documents
Sanket Vaibhav Mehta, Jai Gupta, Yi Tay +6
Differentiable Search Indices (DSIs) encode a corpus of documents in model parameters and use the same model to answer user queries directly. Despite the strong performance of DSI…
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
Aijun Bai, Rolf Jagerman, Zhen Qin +6
As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score…
Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models
Tao Chen, Mingyang Zhang, Jing Lu +2
The pre-trained language model (eg, BERT) based deep retrieval models achieved superior performance over lexical retrieval models (eg, BM25) in many passage retrieval tasks. Howeve…
Rank4Class: A Ranking Formulation for Multiclass Classification
Nan Wang, Zhen Qin, Le Yan +4
Multiclass classification (MCC) is a fundamental machine learning problem of classifying each instance into one of a predefined set of classes. In the deep learning era, extensive…
Improving Neural Ranking via Lossless Knowledge Distillation
Zhen Qin, Le Yan, Yi Tay +4
We explore a novel perspective of knowledge distillation (KD) for learning to rank (LTR), and introduce Self-Distilled neural Rankers (SDR), where student rankers are parameterized…
Dynamic Language Models for Continuously Evolving Content
Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang +2
The content on the web is in a constant state of flux. New entities, issues, and ideas continuously emerge, while the semantics of the existing conversation topics gradually shift.…
Natural Language Understanding with Privacy-Preserving BERT
Chen Qu, Weize Kong, Liu Yang +3
Privacy preservation remains a key challenge in data mining and Natural Language Understanding (NLU). Previous research shows that the input text or even text embeddings can leak p…