Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
LRG at TREC 2020: Document Ranking with XLNet-Based Models
Establishing a good information retrieval system in popular mediums of entertainment is a quickly growing area of investigation for companies and researchers alike. We delve into the domain of information retrieval for p…
DescriptiveDocument RankingInformation RetrievalRe-Ranking+1IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation
This paper introduces our systems for all three subtasks of SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning. To help our model better represent and understand abstract concepts in natural language, we well…
Multiple-choiceQuestion AnsweringReading ComprehensionRe-RankingRevisit Recommender System in the Permutation Prospective
Recommender systems (RS) work effective at alleviating information overload and matching user interests in various web-scale applications. Most RS retrieve the user's favorite candidates and then rank them by the rating …
Recommendation SystemsRe-RankingLeveraging Query Resolution and Reading Comprehension for Conversational Passage Retrieval
This paper describes the participation of UvA.ILPS group at the TREC CAsT 2020 track. Our passage retrieval pipeline consists of (i) an initial retrieval module that uses BM25, and (ii) a re-ranking module that combines …
Passage RetrievalReading ComprehensionRe-RankingRetrievalEvaluating Large-Vocabulary Object Detectors: The Devil is in the Details
By design, average precision (AP) for object detection aims to treat all classes independently: AP is computed independently per category and averaged. On one hand, this is desirable as it treats all classes equally. On …
Benchmarkingobject-detectionObject DetectionRe-RankingBox Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
False positive is one of the most serious problems brought by agnostic domain shift in domain adaptive pedestrian detection. However, it is impossible to label each box in countless target domains. Therefore, it yields o…
object-detectionObject DetectionPedestrian DetectionRe-RankingAssessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval
Our work aimed at experimentally assessing the benefits of model ensembling within the context of neural methods for passage reranking. Starting from relatively standard neural models, we use a previous technique named F…
Learning-To-RankPassage RerankingPassage RetrievalReranking+2Mitigating the Position Bias of Transformer Models in Passage Re-Ranking
Supervised machine learning models and their evaluation strongly depends on the quality of the underlying dataset. When we search for a relevant piece of information it may appear anywhere in a given passage. However, we…
Passage Re-RankingPositionQuestion AnsweringRe-Ranking+2Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces
Diversity in personalized news recommender systems is often defined as dissimilarity, and based on topic diversity (e.g., corona versus farmers strike). Diversity in news media, however, is understood as multiperspectivi…
DiversityRecommendation SystemsRe-RankingThe Highs and Lows of Simple Lexical Domain Adaptation Approaches for Neural Machine Translation
Machine translation systems are vulnerable to domain mismatch, especially in a low-resource scenario. Out-of-domain translations are often of poor quality and prone to hallucinations, due to exposure bias and the decoder…
DecoderDomain AdaptationLanguage ModelingLanguage Modelling+3Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of methods that view each query-document pair in…
Question AnsweringRe-RankingTraditional IR rivals neural models on the MS MARCO Document Ranking Leaderboard
This short document describes a traditional IR system that achieved MRR@100 equal to 0.298 on the MS MARCO Document Ranking leaderboard (on 2020-12-06). Although inferior to most BERT-based models, it outperformed severa…
Document RankingRe-RankingUnderstanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
The re-ranking approach leverages high-confidence retrieved samples to refine retrieval results, which have been widely adopted as a post-processing tool for image retrieval tasks. However, we notice one main flaw of re-…
Drone-view target localizationGPUGraph Neural NetworkImage Retrieval+4Writer Identification and Writer Retrieval Based on NetVLAD with Re-ranking
This paper addresses writer identification and writer retrieval which is considered as a challenging problem in the document analysis and recognition field. In this work, a novel pipeline is proposed for the problem at h…
Re-RankingRetrievalTripletWriter RetrievalLost in Back-Translation: Emotion Preservation in Neural Machine Translation
Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world. An important part of communication, however, takes place at the non-propositio…
DiversityMachine TranslationRe-RankingStyle Transfer+1Improving Passage Re-Ranking with Word N-Gram Aware Coattention Encoder
In text matching applications, coattentions have proved to be highly effective attention mechanisms. Coattention enables the learning to attend based on computing word level affinity scores between two texts. In this pap…
Passage RankingPassage Re-RankingRe-RankingText MatchingPGL at TextGraphs 2020 Shared Task: Explanation Regeneration using Language and Graph Learning Methods
This paper describes the system designed by the Baidu PGL Team which achieved the first place in the TextGraphs 2020 Shared Task. The task focuses on generating explanations for elementary science questions. Given a ques…
Graph LearningLanguage ModelingLanguage ModellingRe-RankingExplanation Regeneration via Multi-Hop ILP Inference over Knowledge Base
Textgraphs 2020 Workshop organized a shared task on ‘Explanation Regeneration’ that required reconstructing gold explanations for elementary science questions. This work describes our submission to the task which is base…
regressionRe-RankingRed Dragon AI at TextGraphs 2020 Shared Task : LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of methods that view each query-document pair in…
Question AnsweringRe-RankingUniversity of Washington at TREC 2020 Fairness Ranking Track
InfoSeeking Lab's FATE (Fairness Accountability Transparency Ethics) group at University of Washington participated in 2020 TREC Fairness Ranking Track. This report describes that track, assigned data and tasks, our grou…
EthicsFairnessRe-RankingRetrieval