Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Groupwise Query Performance Prediction with BERT
While large-scale pre-trained language models like BERT have advanced the state-of-the-art in IR, its application in query performance prediction (QPP) is so far based on pointwise modeling of individual queries. Meanwhi…
Learning-To-RankPredictionRe-RankingRegulating Group Exposure for Item Providers in Recommendation
Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendation services, the interests of those provi…
Re-RankingPost Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x" starred by actress "y" recommende…
DiversityExplainable ModelsExplainable RecommendationInformation Retrieva+7CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender …
FairnessRecommendation SystemsRe-RankingImproving Passage Retrieval with Zero-Shot Question Generation
We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. The re-ranker re-scores retrieved passages with a zero-shot question generation model, which uses a pre-trai…
Language ModelingLanguage ModellingOpen-Domain Question AnsweringOpen-Ended Question Answering+6Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformers
This paper presents the first multi-objective transformer model for constructing open cloze tests that exploits generation and discrimination capabilities to improve performance. Our model is further enhanced by tweaking…
Overall - TestRe-RankingReuse your features: unifying retrieval and feature-metric alignment
We propose a compact pipeline to unify all the steps of Visual Localization: image retrieval, candidate re-ranking and initial pose estimation, and camera pose refinement. Our key assumption is that the deep features use…
Image RetrievalPose EstimationRe-RankingRetrieval+1Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem
We propose an autoregressive entity linking model, that is trained with two auxiliary tasks, and learns to re-rank generated samples at inference time. Our proposed novelties address two weaknesses in the literature. Fir…
DecoderEntity LinkingRe-RankingReinforcement Re-ranking with 2D Grid-based Recommendation Panels
Modern recommender systems usually present items as a streaming, one-dimensional ranking list. Recently there is a trend in e-commerce that the recommended items are organized grid-based panels with two dimensions where …
Recommendation SystemsRe-RankingTowards Better Chinese-centric Neural Machine Translation for Low-resource Languages
The last decade has witnessed enormous improvements in science and technology, stimulating the growing demand for economic and cultural exchanges in various countries. Building a neural machine translation (NMT) system h…
Low Resource NMTMachine TranslationNMTRe-Ranking+2Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval
State-of-the-art neural (re)rankers are notoriously data-hungry which -- given the lack of large-scale training data in languages other than English -- makes them rarely used in multilingual and cross-lingual retrieval s…
Cross-Lingual TransferLanguage ModellingMachine TranslationReranking+2Correlation Verification for Image Retrieval
Geometric verification is considered a de facto solution for the re-ranking task in image retrieval. In this study, we propose a novel image retrieval re-ranking network named Correlation Verification Networks (CVNet). O…
Feature CorrelationGeometric MatchingImage RetrievalRe-Ranking+1Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking
Transformer based re-ranking models can achieve high search relevance through context-aware soft matching of query tokens with document tokens. To alleviate runtime complexity of such inference, previous work has adopted…
QuantizationRe-RankingPEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
The goal of recommender systems is to provide ordered item lists to users that best match their interests. As a critical task in the recommendation pipeline, re-ranking has received increasing attention in recent years. …
Recommendation SystemsRe-RankingSummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially through fine-tuning large pre-trained language models on the downstream dataset. These models are typical…
Abstractive Text SummarizationDocument SummarizationMixture-of-ExpertsRe-Ranking+1LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval
In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever that does not require any supervised data for training. Specifically, we first present Iterative Contrastive Learning (ICoL) that iteratively tr…
Contrastive LearningRe-RankingRetrievalText Retrieval+1Towards Robust Online Dialogue Response Generation
Although pre-trained sequence-to-sequence models have achieved great success in dialogue response generation, chatbots still suffer from generating inconsistent responses in real-world practice, especially in multi-turn …
ChatbotRe-RankingResponse GenerationSelf-Supervised Class-Cognizant Few-Shot Classification
Unsupervised learning is argued to be the dark matter of human intelligence. To build in this direction, this paper focuses on unsupervised learning from an abundance of unlabeled data followed by few-shot fine-tuning on…
ClassificationContrastive LearningRe-RankingUnsupervised Few-Shot Image ClassificationTightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition
Visual place recognition (VPR) is a key issue for robotics and autonomous systems. For the trade-off between time and performance, most of methods use the coarse-to-fine hierarchical architecture, which consists of retri…
Computational EfficiencyDynamic Time WarpingRe-RankingRetrieval+2DS4DH at TREC Health Misinformation 2021: Multi-Dimensional Ranking Models with Transfer Learning and Rank Fusion
This paper describes the work of the Data Science for Digital Health (DS4DH) group at the TREC Health Misinformation Track 2021. The TREC Health Misinformation track focused on the development of retrieval methods that p…
Information RetrievalMisinformationRe-RankingRetrieval+1