Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation
Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e.g., click, like and purchase). Besides considering the relevance between recom…
DisentanglementDiversityRe-RankingFew-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems
Existing work on generating hints in Intelligent Tutoring Systems (ITS) focuses mostly on manual and non-personalized feedback. In this work, we explore automatically generated questions as personalized feedback in an IT…
Generative Question AnsweringQuestion AnsweringQuestion GenerationQuestion-Generation+2Fast and Light-Weight Answer Text Retrieval in Dialogue Systems
Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available. T…
Re-RankingRetrievalText RetrievalLeiBi@COLIEE 2022: Aggregating Tuned Lexical Models with a Cluster-driven BERT-based Model for Case Law Retrieval
This paper summarizes our approaches submitted to the case law retrieval task in the Competition on Legal Information Extraction/Entailment (COLIEE) 2022. Our methodology consists of four steps; in detail, given a legal …
Re-RankingRetrievalSentenceUniInst: Unique Representation for End-to-End Instance Segmentation
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e.g., multiple boxes, grids, and anchor points) are inferred for one instance…
Instance SegmentationRe-RankingSegmentationSemantic SegmentationOptimizing Test-Time Query Representations for Dense Retrieval
Recent developments of dense retrieval rely on quality representations of queries and contexts from pre-trained query and context encoders. In this paper, we introduce TOUR (Test-Time Optimization of Query Representation…
Contrastive LearningInformation RetrievalOpen-Domain Question AnsweringPassage Retrieval+3Few-shot Reranking for Multi-hop QA via Language Model Prompting
We study few-shot reranking for multi-hop QA with open-domain questions. To alleviate the need for a large number of labeled question-document pairs for retriever training, we propose PromptRank, which relies on large la…
Language ModelingOpen-Domain Question AnsweringPassage Re-RankingReranking+2What Makes Data-to-Text Generation Hard for Pretrained Language Models?
Expressing natural language descriptions of structured facts or relations -- data-to-text generation (D2T) -- increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained languag…
Data-to-Text GenerationFew-Shot LearningRe-RankingText GenerationAutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System
Industrial search and recommendation systems mostly follow the classic multi-stage information retrieval paradigm: matching, pre-ranking, ranking, and re-ranking stages. To account for system efficiency, simple vector-pr…
Information RetrievalNeural Architecture SearchRecommendation SystemsRe-Ranking+1Experiments on Generalizability of User-Oriented Fairness in Recommender Systems
Recent work in recommender systems mainly focuses on fairness in recommendations as an important aspect of measuring recommendations quality. A fairness-aware recommender system aims to treat different user groups simila…
FairnessRecommendation SystemsRe-RankingControlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022
This paper describes the SLT-CDT-UoS group's submission to the first Special Task on Formality Control for Spoken Language Translation, part of the IWSLT 2022 Evaluation Campaign. Our efforts were split between two front…
Domain AdaptationLow Resource NMTNMTRe-Ranking+2Long Document Re-ranking with Modular Re-ranker
Long document re-ranking has been a challenging problem for neural re-rankers based on deep language models like BERT. Early work breaks the documents into short passage-like chunks. These chunks are independently mapped…
Document RankingRe-RankingCODEC: Complex Document and Entity Collection
CODEC is a document and entity ranking benchmark that focuses on complex research topics. We target essay-style information needs of social science researchers, i.e. "How has the UK's Open Banking Regulation benefited Ch…
Document RankingRe-RankingRetrievalSubverting Fair Image Search with Generative Adversarial Perturbations
In this work we explore the intersection fairness and robustness in the context of ranking: when a ranking model has been calibrated to achieve some definition of fairness, is it possible for an external adversary to mak…
FairnessImage RetrievalRe-RankingImproving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity
Ensuring relevance quality in product search is a critical task as it impacts the customer’s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the…
Re-RankingSemantic SimilaritySemantic Textual SimilarityDocument Retrieval and Claim Verification to Mitigate COVID-19 Misinformation
During the COVID-19 pandemic, the spread of misinformation on online social media has grown exponentially. Unverified bogus claims on these platforms regularly mislead people, leading them to believe in half-baked truths…
Claim VerificationMisinformationNatural Language InferenceRe-Ranking+1Curriculum Learning for Dense Retrieval Distillation
Recent work has shown that more effective dense retrieval models can be obtained by distilling ranking knowledge from an existing base re-ranking model. In this paper, we propose a generic curriculum learning based optim…
Knowledge DistillationPassage RetrievalRe-RankingRetrievalCross-Camera Trajectories Help Person Retrieval in a Camera Network
We are concerned with retrieving a query person from multiple videos captured by a non-overlapping camera network. Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spa…
Person RetrievalRe-RankingRetrievalIncorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking
Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their overwhelming advantages in natural languag…
Graph Neural NetworkNatural Language UnderstandingPassage Re-RankingRe-Ranking+1ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference
State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. To this end, models generally utilize an encoder-only (like BERT) paradigm or an encoder-decoder (like T5) approa…
DecoderInformation RetrievalLanguage ModelingLanguage Modelling+2