Zhuyun Dai

Learning To Rank Resources

2017

We present a learning-to-rank approach for resource selection. We develop features for resource ranking and present a training approach that does not require human judgments. Our method is well-suited to environments with a large number …

Promptagator: Few-shot Dense Retrieval From 8 Examples

2022 · arXiv (Cornell University)

Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is limited, with the implicit assumption that it is …

Multi-Vector Retrieval as Sparse Alignment

2022 · arXiv (Cornell University)

Multi-vector retrieval models improve over single-vector dual encoders on many information retrieval tasks. In this paper, we cast the multi-vector retrieval problem as sparse alignment between query and document tokens. We propose AligneR, a novel …

Query-Biased Partitioning for Selective Search

2016

Selective search is a cluster-based distributed retrieval architecture that reduces computational costs by partitioning a corpus into topical shards, and selectively searching them. Prior research formed topical shards by clustering the corpus based on the …

End-to-End Neural Ad-hoc Ranking with Kernel Pooling

2017

This paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling …

Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search

2018

This paper presents \textttConv-KNRM, a Convolutional Kernel-based Neural Ranking Model that models n-gram soft matches for ad-hoc search. Instead of exact matching query and document n-grams, \textttConv-KNRM uses Convolutional Neural Networks to represent n-grams of …

Deeper Text Understanding for IR with Contextual Neural Language Modeling

2019

Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations. Neural IR models have achieved promising results in learning query-document relevance patterns, but few explorations have been done on understanding the …

COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List

2021

Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index. Recent neural IR models shifts towards soft semantic matching all query document terms, but …

Scaling Instruction-Finetuned Language Models

2022 · arXiv (Cornell University)

Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on …

Large Dual Encoders Are Generalizable Retrievers

2022

Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. …