Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Context-Aware Dialog Re-Ranking for Task-Oriented Dialog Systems
Dialog response ranking is used to rank response candidates by considering their relation to the dialog history. Although researchers have addressed this concept for open-domain dialogs, little attention has been focused…
Re-Rankingspeech-recognitionSpeech RecognitionEfficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years. A downside to diffusion is that it performs sl…
Image RetrievalRe-RankingRetrievalScalable graph-based individual named entity identification
Named entity discovery (NED) is an important information retrieval problem that can be decomposed into two sub-problems. The first sub-problem, named entity recognition (NER), aims to tag pre-defined sets of words in a v…
Entity LinkingGraph MiningInformation Retrievalnamed-entity-recognition+6Adaptive Re-ranking of Deep Feature for Person Re-identification
Typical person re-identification (re-ID) methods train a deep CNN to extract deep features and combine them with a distance metric for the final evaluation. In this work, we focus on exploiting the full information encod…
Person Re-IdentificationRe-RankingRetrievalBoosting Search Performance Using Query Variations
Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. However, to date fusion has not been regarded as being cost-effective in cases where strict per-que…
Re-RankingRetrievalEffective Representation for Easy-First Dependency Parsing
Easy-first parsing relies on subtree re-ranking to build the complete parse tree. Whereas the intermediate state of parsing processing is represented by various subtrees, whose internal structural information is the key …
Dependency ParsingLanguage ModelingLanguage ModellingRe-RankingChar2char Generation with Reranking for the E2E NLG Challenge
This paper describes our submission to the E2E NLG Challenge. Recently, neural seq2seq approaches have become mainstream in NLG, often resorting to pre- (respectively post-) processing delexicalization (relexicalization)…
RerankingRe-RankingText GenerationRetrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs
SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG). Existing QA systems rely on various components to solve different sub-tasks of the problem (such as enti…
Entity LinkingInformation RetrievalKnowledge GraphsLearning-To-Rank+5Visual Re-ranking with Natural Language Understanding for Text Spotting
Many scene text recognition approaches are based on purely visual information and ignore the semantic relation between scene and text. In this paper, we tackle this problem from natural language processing perspective to…
Language ModelingLanguage ModellingNatural Language UnderstandingRe-Ranking+2From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing
The availability of massive data and computing power allowing for effective data driven neural approaches is having a major impact on machine learning and information retrieval research, but these models have a basic pr…
Ad-Hoc Information RetrievalBlockingInformation RetrievalRe-Ranking+1Seq2Slate: Re-ranking and Slate Optimization with RNNs
Ranking is a central task in machine learning and information retrieval. In this task, it is especially important to present the user with a slate of items that is appealing as a whole. This in turn requires taking into …
Information RetrievalRe-RankingRetrievalNeural Quality Estimation of Grammatical Error Correction
Grammatical error correction (GEC) systems deployed in language learning environments are expected to accurately correct errors in learners{'} writing. However, in practice, they often produce spurious corrections and fa…
Grammatical Error CorrectionMachine TranslationRe-RankingLearning to Embed Semantic Correspondence for Natural Language Understanding
While learning embedding models has yielded fruitful results in several NLP subfields, most notably Word2Vec, embedding correspondence has relatively not been well explored especially in the context of natural language u…
Natural Language UnderstandingRe-RankingSemantic correspondenceSlot FillingNTT's Neural Machine Translation Systems for WMT 2018
This paper describes NTT{'}s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks. Our submission has three main components: the Transformer model, corpus …
Machine TranslationRe-RankingTranslationSheffield Submissions for WMT18 Multimodal Translation Shared Task
This paper describes the University of Sheffield{'}s submissions to the WMT18 Multimodal Machine Translation shared task. We participated in both tasks 1 and 1b. For task 1, we build on a standard sequence to sequence at…
Data AugmentationMachine TranslationMultimodal Machine TranslationNMT+3Personalizing Fairness-aware Re-ranking
Personalized recommendation brings about novel challenges in ensuring fairness, especially in scenarios in which users are not the only stakeholders involved in the recommender system. For example, the system may want to…
DiversityFairnessRecommendation SystemsRe-RankingUsing Image Fairness Representations in Diversity-Based Re-ranking for Recommendations
The trade-off between relevance and fairness in personalized recommendations has been explored in recent works, with the goal of minimizing learned discrimination towards certain demographics while still producing releva…
DiversityFairnessRecommendation SystemsRe-RankingDeep neural network marketplace recommenders in online experiments
Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs. To understand user intent and tailor recommendations to their needs, we use deep learning to explore vario…
Re-RankingA Practical Deep Online Ranking System in E-commerce Recommendation
User online shopping experience in modern e-commerce websites critically relies on real-time personalized recommendations. However, building a productionized recommender system still remains challenging due to a massive …
Recommendation SystemsRe-RankingRetrievalDiscriminative multi-view Privileged Information learning for image re-ranking
Conventional multi-view re-ranking methods usually perform asymmetrical matching between the region of interest (ROI) in the query image and the whole target image for similarity computation. Due to the inconsistency in …
Re-RankingRetrieval