Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
BERT-QE: Contextualized Query Expansion for Document Re-ranking
Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant information when expanding the query. To bri…
Re-RankingRetrievalBrown University at TREC Deep Learning 2019
This paper describes Brown University's submission to the TREC 2019 Deep Learning track. We followed a 2-phase method for producing a ranking of passages for a given input query: In the the first phase, the user's query …
Deep LearningInformation RetrievalLanguage ModelingLanguage Modelling+3Towards Unique and Informative Captioning of Images
Despite considerable progress, state of the art image captioning models produce generic captions, leaving out important image details. Furthermore, these systems may even misrepresent the image in order to produce a simp…
DiversityImage CaptioningRe-RankingContextual Personalized Re-Ranking of Music Recommendations through Audio Features
Users are able to access millions of songs through music streaming services like Spotify, Pandora, and Deezer. Access to such large catalogs, created a need for relevant song recommendations. However, user preferences ar…
Re-RankingAn exploratory study of L1-specific non-words
In this paper, we explore L1-specific non-words, i.e. non-words in a target language (in this case Swedish) that are re-ranked by a different-language language model. We surmise that speakers of a certain L1 will react d…
Language ModelingLanguage ModellingRe-RankingGraph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix
Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep neural network approaches with signific…
Drug DesignPredictionRe-RankingExploring Longitudinal Effects of Session-based Recommendations
Session-based recommendation is a problem setting where the task of a recommender system is to make suitable item suggestions based only on a few observed user interactions in an ongoing session. The lack of long-term pr…
Recommendation SystemsRe-RankingSession-Based RecommendationsRethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter
The real human attention is an interactive activity between our visual system and our brain, using both low-level visual stimulus and high-level semantic information. Previous image salient object detection (SOD) works c…
Objectobject-detectionObject DetectionRe-Ranking+2GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy
The automatic grading of diabetic retinopathy (DR) facilitates medical diagnosis for both patients and physicians. Existing researches formulate DR grading as an image classification problem. As the stages/categories of …
ClassificationGeneral Classificationimage-classificationImage Classification+2Multi-Perspective Semantic Information Retrieval in the Biomedical Domain
Information Retrieval (IR) is the task of obtaining pieces of data (such as documents) that are relevant to a particular query or need from a large repository of information. IR is a valuable component of several downstr…
ArticlesInformation RetrievalLanguage ModellingPassage Re-Ranking+4Re-ranking for Writer Identification and Writer Retrieval
Automatic writer identification is a common problem in document analysis. State-of-the-art methods typically focus on the feature extraction step with traditional or deep-learning-based techniques. In retrieval problems,…
Re-RankingRetrievalWriter RetrievalImage-based Vehicle Re-identification Model with Adaptive Attention Modules and Metadata Re-ranking
Vehicle Re-identification is a challenging task due to intra-class variability and inter-class similarity across non-overlapping cameras. To tackle these problems, recently proposed methods require additional annotation …
Re-RankingVehicle Re-IdentificationSimple and Effective Retrieve-Edit-Rerank Text Generation
Retrieve-and-edit seq2seq methods typically retrieve an output from the training set and learn a model to edit it to produce the final output. We propose to extend this framework with a simple and effective post-generati…
Machine TranslationRe-RankingText GenerationTranslationCheckpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems
In this paper, we propose a method of re-ranking the outputs of Neural Machine Translation (NMT) systems. After the decoding process, we select a few last iteration outputs in the training process as the $N$-best list. A…
DecoderLanguage ModellingMachine TranslationNMT+3Xiaomi's Submissions for IWSLT 2020 Open Domain Translation Task
This paper describes the Xiaomi{'}s submissions to the IWSLT20 shared open domain translation task for Chinese{\textless}-{\textgreater}Japanese language pair. We explore different model ensembling strategies based on re…
Domain AdaptationKnowledge DistillationRe-RankingTranslationSample Efficient Graph-Based Optimization with Noisy Observations
We study sample complexity of optimizing "hill-climbing friendly" functions defined on a graph under noisy observations. We define a notion of convexity, and we show that a variant of best-arm identification can find a n…
Re-RankingFairness-Aware Explainable Recommendation over Knowledge Graphs
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in particular, may suffer from both explanation …
Collaborative FilteringDecision MakingExplainable RecommendationFairness+3Visual-Semantic Matching by Exploring High-Order Attention and Distraction
Cross-modality semantic matching is a vital task in computer vision and has attracted increasing attention in recent years. Existing methods mainly explore object-based alignment between image objects and text words. In …
AttributeGraph AttentionGraph MatchingGraph Similarity+5Analyzing and Learning from User Interactions for Search Clarification
Asking clarifying questions in response to search queries has been recognized as a useful technique for revealing the underlying intent of the query. Clarification has applications in retrieval systems with different int…
Re-RankingRetrievalLearning Efficient Representations of Mouse Movements to Predict User Attention
Tracking mouse cursor movements can be used to predict user attention on heterogeneous page layouts like SERPs. So far, previous work has relied heavily on handcrafted features, which is a time-consuming approach that of…
Re-RankingTime SeriesTime Series Analysis