Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
P-MMF: Provider Max-min Fairness Re-ranking in Recommender System
In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder recommender systems. Existing studies on provider fairness usually focu…
FairnessRecommendation SystemsRe-RankingGlobalNER: Incorporating Non-local Information into Named Entity Recognition
Nowadays, many Natural Language Processing (NLP) tasks see the demand for incorporating knowledge external to the local information to further improve the performance. However, there is little related work on Named Entit…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+1Slate-Aware Ranking for Recommendation
We see widespread adoption of slate recommender systems, where an ordered item list is fed to the user based on the user interests and items' content. For each recommendation, the user can select one or several items fro…
Recommendation SystemsRe-RankingPrototype Learning for Automatic Check-Out
The basic goal of Automatic Check-Out (ACO) task is to accurately predict the categories and quantities of products selected by customers in the check-out images. However, there is a significant domain gap between the si…
Re-RankingKG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting
Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions. When there is an entity error, it imposes extra challenges for a dialogue system to produce satisfactory responses. In th…
Entity RetrievalFew-Shot LearningRe-RankingRetrievalActional Atomic-Concept Learning for Demystifying Vision-Language Navigation
Vision-Language Navigation (VLN) is a challenging task which requires an agent to align complex visual observations to language instructions to reach the goal position. Most existing VLN agents directly learn to align th…
Re-RankingVision-Language NavigationRelatedly: Scaffolding Literature Reviews with Existing Related Work Sections
Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes increasingly challenging. Meanwhile, aut…
DescriptiveNavigateRe-RankingPerformative Recommendation: Diversifying Content via Strategic Incentives
The primary goal in recommendation is to suggest relevant content to users, but optimizing for accuracy often results in recommendations that lack diversity. To remedy this, conventional approaches such as re-ranking imp…
DiversityRe-RankingPIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
Re-ranking draws increased attention on both academics and industries, which rearranges the ranking list by modeling the mutual influence among items to better meet users' demands. Many existing re-ranking methods direct…
RerankingRe-RankingLexical Simplification using multi level and modular approach
Text Simplification is an ongoing problem in Natural Language Processing, solution to which has varied implications. In conjunction with the TSAR-2022 Workshop @EMNLP2022 Lexical Simplification is the process of reducing…
Lexical SimplificationRe-RankingTAGText SimplificationUsing In-Context Learning to Improve Dialogue Safety
While large neural-based conversational models have become increasingly proficient dialogue agents, recent work has highlighted safety issues with these systems. For example, these systems can be goaded into generating t…
In-Context LearningRe-RankingRetrievalEmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
Large neural models (such as Transformers) achieve state-of-the-art performance for information retrieval (IR). In this paper, we aim to improve distillation methods that pave the way for the resource-efficient deploymen…
Information RetrievalKnowledge DistillationRe-RankingRetrievalImproving Noise Robustness for Spoken Content Retrieval using Semi-supervised ASR and N-best Transcripts for BERT-based Ranking Models
BERT-based re-ranking and dense retrieval (DR) systems have been shown to improve search effectiveness for spoken content retrieval (SCR). However, both methods can still show a reduction in effectiveness when using ASR …
Re-RankingRetrievalInPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers
We carried out a reproducibility study of InPars, which is a method for unsupervised training of neural rankers (Bonifacio et al., 2022). As a by-product, we developed InPars-light, which is a simple-yet-effective modifi…
Language ModellingRe-RankingRetrievalUnsupervised Summarization Re-ranking
With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. However, the performance of such unsupervised…
Abstractive Text SummarizationRe-RankingImproved Beam Search for Hallucination Mitigation in Abstractive Summarization
Advancement in large pretrained language models has significantly improved their performance for conditional language generation tasks including summarization albeit with hallucinations. To reduce hallucinations, convent…
Abstractive Text SummarizationDiversityHallucinationNatural Language Inference+2LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from Short to Long Contexts and for Implication-Based Retrieval
Natural language inference has trended toward studying contexts beyond the sentence level. An important application area is law: past cases often do not foretell how they apply to new situations and implications must be …
Domain GeneralizationNatural Language InferenceRe-RankingRetrieval+1RHO ($ρ$): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce hallucinated responses not supported by the inp…
HallucinationRepresentation LearningRe-RankingStructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition
Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from …
Image RetrievalKnowledge DistillationRe-RankingRetrieval+1Diverse Multi-Answer Retrieval with Determinantal Point Processes
Often questions provided to open-domain question answering systems are ambiguous. Traditional QA systems that provide a single answer are incapable of answering ambiguous questions since the question may be interpreted i…
Open-Domain Question AnsweringPoint ProcessesQuestion AnsweringRe-Ranking+1