Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Text-to-Text Multi-view Learning for Passage Re-ranking
Recently, much progress in natural language processing has been driven by deep contextualized representations pretrained on large corpora. Typically, the fine-tuning on these pretrained models for a specific downstream t…
MULTI-VIEW LEARNINGPassage RankingPassage Re-RankingRe-Ranking+2Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers
Societal biases resonate in the retrieved contents of information retrieval (IR) systems, resulting in reinforcing existing stereotypes. Approaching this issue requires established measures of fairness in respect to the …
DisentanglementFairnessInformation RetrievalModel Selection+4Reranking Machine Translation Hypotheses with Structured and Web-based Language Models
In this paper, we investigate the use of linguistically motivated and computationally efficient structured language models for reranking N-best hypotheses in a statistical machine translation system. These language model…
Language ModelingLanguage ModellingMachine TranslationReranking+2Learning Passage Impacts for Inverted Indexes
Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-rank this set using contextualized languag…
Information RetrievalLanguage ModelingLanguage ModellingRe-Ranking+1User-oriented Fairness in Recommendation
As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects the system performance. Therefore, it i…
FairnessRecommendation SystemsRe-RankingBEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution (OOD) generalization capabilities. To a…
Argument RetrievalBenchmarkingBiomedical Information RetrievalCitation Prediction+11Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context
BERT-based text ranking models have dramatically advanced the state-of-the-art in ad-hoc retrieval, wherein most models tend to consider individual query-document pairs independently. In the mean time, the importance and…
Learning-To-RankRe-RankingRetrievalUltra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval
The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches. However, neural models' dense representations are more suitable for re-ranking, due…
BinarizationInformation RetrievalLanguage ModellingRe-Ranking+3TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning
Learning sentence embeddings often requires a large amount of labeled data. However, for most tasks and domains, labeled data is seldom available and creating it is expensive. In this work, we present a new state-of-the-…
DenoisingDomain AdaptationInformation RetrievalLanguage Modeling+9Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout the training, indexing and query workflows. The neural IR community made great advancements in training effe…
GPURe-RankingRetrievalText Retrieval+1BERT Embeddings Can Track Context in Conversational Search
The use of conversational assistants to search for information is becoming increasingly more popular among the general public, pushing the research towards more advanced and sophisticated techniques. In the last few year…
Conversational SearchRe-RankingRetrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering
Question answering over knowledge bases (KBQA) usually involves three sub-tasks, namely topic entity detection, entity linking and relation detection. Due to the large number of entities and relations inside knowledge ba…
Entity LinkingInformation RetrievalKnowledge Base Question AnsweringMulti-Task Learning+3Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers
Our objective is language-based search of large-scale image and video datasets. For this task, the approach that consists of independently mapping text and vision to a joint embedding space, a.k.a. dual encoders, is attr…
Re-RankingRetrievalFrom Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search
We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world. Specifically, we outline the design of a deep learning system that ret…
Deep LearningRe-RankingRetrievalSemantic RetrievalAttention-based neural re-ranking approach for next city in trip recommendations
This paper describes an approach to solving the next destination city recommendation problem for a travel reservation system. We propose a two stages approach: a heuristic approach for candidates selection and an attenti…
Learning-To-RankRe-RankingUnsupervised and self-adaptative techniques for cross-domain person re-identification
Person Re-Identification (ReID) across non-overlapping cameras is a challenging task and, for this reason, most works in the prior art rely on supervised feature learning from a labeled dataset to match the same person i…
Decision MakingDomain AdaptationPerson Re-IdentificationRe-Ranking+1LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computation c…
Image-text RetrievalRe-RankingRetrievalText RetrievalComposite Re-Ranking for Efficient Document Search with BERT
Although considerable efforts have been devoted to transformer-based ranking models for document search, the relevance-efficiency tradeoff remains a critical problem for ad-hoc ranking. To overcome this challenge, this p…
Re-RankingCEQE: Contextualized Embeddings for Query Expansion
In this work we leverage recent advances in context-sensitive language models to improve the task of query expansion. Contextualized word representation models, such as ELMo and BERT, are rapidly replacing static embeddi…
RerankingRe-RankingRetrievalSemantic Models for the First-stage Retrieval: A Comprehensive Review
Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents, and latter stages attempt to re-rank those candidates. …
Re-RankingRetrievalSemantic Retrieval