Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Dealing with Typos for BERT-based Passage Retrieval and Ranking
Passage retrieval and ranking is a key task in open-domain question answering and information retrieval. Current effective approaches mostly rely on pre-trained deep language model-based retrievers and rankers. These met…
Information RetrievalLanguage ModelingLanguage ModellingOpen-Domain Question Answering+6Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion
BERT-based information retrieval models are expensive, in both time (query latency) and computational resources (energy, hardware cost), making many of these models impractical especially under resource constraints. The …
CPUInformation RetrievalPassage Re-RankingRe-Ranking+1On Single and Multiple Representations in Dense Passage Retrieval
The advent of contextualised language models has brought gains in search effectiveness, not just when applied for re-ranking the output of classical weighting models such as BM25, but also when used directly for passage …
Passage RetrievalRe-RankingRetrievalDoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval
In this paper, we present our approaches for the case law retrieval and the legal case entailment task in the Competition on Legal Information Extraction/Entailment (COLIEE) 2021. As first stage retrieval methods combine…
Information RetrievalPassage RetrievalRe-RankingRetrievalTextual Representations for Crosslingual Information Retrieval
In this paper, we explored different levels of textual representations for cross-lingual information retrieval. Beyond the traditional token level representation, we adopted the subword and character level representation…
Cross-Lingual Information RetrievalInformation RetrievalMachine TranslationRe-Ranking+2A Discriminative Semantic Ranker for Question Retrieval
Similar question retrieval is a core task in community-based question answering (CQA) services. To balance the effectiveness and efficiency, the question retrieval system is typically implemented as multi-stage rankers: …
Question AnsweringRe-RankingRetrievalGraph Convolution for Re-ranking in Person Re-identification
Nowadays, deep learning is widely applied to extract features for similarity computation in person re-identification (re-ID) and have achieved great success. However, due to the non-overlapping between training and testi…
Person Re-IdentificationRe-RankingRetrievalA Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich literature of classical generative retrieval m…
Passage Re-RankingPassage RetrievalRe-RankingRetrieval+1Exploiting Sentence-Level Representations for Passage Ranking
Recently, pre-trained contextual models, such as BERT, have shown to perform well in language related tasks. We revisit the design decisions that govern the applicability of these models for the passage re-ranking task i…
Open-Domain Question AnsweringPassage RankingPassage Re-RankingQuestion Answering+2Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network
Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge and u…
Knowledge Graph CompletionRe-RankingRECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negati…
Machine Reading ComprehensionNatural QuestionsOpen-Domain Question AnsweringQuestion Answering+3DeepBlueAI at TextGraphs 2021 Shared Task: Treating Multi-Hop Inference Explanation Regeneration as A Ranking Problem
This paper describes the winning system for TextGraphs 2021 shared task: Multi-hop inference explanation regeneration. Given a question and its corresponding correct answer, this task aims to select the facts that can ex…
Re-RankingTripletA Three-step Method for Multi-Hop Inference Explanation Regeneration
Multi-hop inference for explanation generation is to combine two or more facts to make an inference. The task focuses on generating explanations for elementary science questions. In the task, the relevance between the ex…
Explanation GenerationRe-RankingIBMResearch at MEDIQA 2021: Toward Improving Factual Correctness of Radiology Report Abstractive Summarization
Although recent advances in abstractive summarization systems have achieved high scores on standard natural language metrics like ROUGE, their lack of factual consistency remains an open challenge for their use in sensit…
Abstractive Text SummarizationDecoderRe-RankingCNN Retrieval based Unsupervised Metric Learning for Near-Duplicated Video Retrieval
As important data carriers, the drastically increasing number of multimedia videos often brings many duplicate and near-duplicate videos in the top results of search. Near-duplicate video retrieval (NDVR) can cluster and…
Metric LearningRe-RankingRetrievalVideo RetrievalAn Empirical Study of Vehicle Re-Identification on the AI City Challenge
This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four poin…
DiversityRe-RankingRetrievalVehicle Re-IdentificationIntra-Document Cascading: Learning to Select Passages for Neural Document Ranking
An emerging recipe for achieving state-of-the-art effectiveness in neural document re-ranking involves utilizing large pre-trained language models - e.g., BERT - to evaluate all individual passages in the document and th…
Document RankingKnowledge DistillationRe-RankingEnhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention
The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction. While this ranking step is traditionally performed using heuristics, or by sorti…
Information RetrievalLearning-To-RankRe-RankingRetrievalTowards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach
In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted. Such architecture usually consists of matching, pre-ranking, ranking, and re-ranking stages. In the…
feature selectionRe-RankingMoving Towards Centers: Re-ranking with Attention and Memory for Re-identification
Re-ranking utilizes contextual information to optimize the initial ranking list of person or vehicle re-identification (re-ID), which boosts the retrieval performance at post-processing steps. This paper proposes a re-ra…
Re-RankingRetrievalVehicle Re-Identification