Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Improving Person Re-Identification with Temporal Constraints
In this paper we introduce an image-based person re-identification dataset collected across five non-overlapping camera views in the large and busy airport in Dublin, Ireland. Unlike all publicly available image-based da…
Person Re-IdentificationRe-RankingNVIDIA NeMo Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21
This paper provides an overview of NVIDIA NeMo's neural machine translation systems for the constrained data track of the WMT21 News and Biomedical Shared Translation Tasks. Our news task submissions for English-German (…
Data AugmentationKnowledge DistillationMachine TranslationRe-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…
DecoderLanguage ModelingLanguage ModellingRe-RankingTowards Robust Online Dialogue Response Generation
Although pre-trained sequence-to-sequence models have achieved great success in dialogue response generation, chatbots still suffer from generating inconsistent responses in real-world practice, especially in multi-turn …
Re-RankingResponse GenerationDetection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem
We propose an autoregressive entity linking model, that is trained with two auxiliary tasks, and learns to re-rank generated samples at inference time. Our proposed novelties address two weaknesses in the literature. Fir…
Entity LinkingRe-RankingSummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially with the trend of fine-tuning large pre-trained language models on the downstream dataset. These models a…
Abstractive Text SummarizationMixture-of-ExpertsRe-RankingTowards Robust Passage Re-Ranking Model by Mitigating Lexical Match Bias
While deep learning models can overcome the limitations of traditional machine learning algorithms that use hand-crafted features, recent studies have shown that these models often achieve high dataset-specific accuracy …
Information RetrievalPassage Re-RankingRe-RankingRetrievalCompact Token Representations with Contextual Quantization for Efficient Document Re-ranking
Transformer based re-ranking models can achieve high search relevance through context-aware soft matching of query tokens with document tokens. To alleviate runtime complexity of such inference, previous work has adopte…
QuantizationRe-RankingA Free Format Legal Question Answering System
We present an information retrieval-based question answer system to answer legal questions. The system is not limited to a predefined set of questions or patterns and uses both sparse vector search and embeddings for inp…
Information RetrievalQuestion AnsweringRe-RankingRetrievalDefinition Modelling for Appropriate Specificity
Definition generation techniques aim to generate a definition of a target word or phrase given a context. In previous studies, researchers have faced various issues such as the out-of-vocabulary problem and over/under-sp…
DecoderDefinition ModellingRe-RankingSpecificityMulti-Vector Attention Models for Deep Re-ranking
Large-scale document retrieval systems often utilize two styles of neural network models which live at two different ends of the joint computation vs. accuracy spectrum. The first style is dual encoder (or two-tower) mod…
Passage RetrievalRe-RankingRetrievalMulti-Class Grammatical Error Detection for Correction: A Tale of Two Systems
In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English. Specifically, we first develop a new state-of-the-art binary detect…
DecoderGrammatical Error CorrectionGrammatical Error DetectionNMT+1NVIDIA NeMo’s Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21
This paper provides an overview of NVIDIA NeMo’s neural machine translation systems for the constrained data track of the WMT21 News and Biomedical Shared Translation Tasks. Our news task submissions for English-German (…
Data AugmentationKnowledge DistillationMachine TranslationRe-Ranking+1Papago’s Submissions to the WMT21 Triangular Translation Task
This paper describes Naver Papago’s submission to the WMT21 shared triangular MT task to enhance the non-English MT system with tri-language parallel data. The provided parallel data are Russian-Chinese (direct), Russian…
Re-RankingTranslationContextual Similarity Aggregation with Self-attention for Visual Re-ranking
In content-based image retrieval, the first-round retrieval result by simple visual feature comparison may be unsatisfactory, which can be refined by visual re-ranking techniques. In image retrieval, it is observed that …
Content-Based Image RetrievalData AugmentationImage RetrievalRe-Ranking+1Developing a Meta-suggestion Engine for Search Queries
Typically, search engines provide query suggestions to assist users in the search process. Query suggestions are very important for improving users search experience. However, most query suggestions are based on the user…
RerankingRe-RankingConfidence-Aware Active Feedback for Interactive Instance Search
Online relevance feedback (RF) is widely utilized in instance search (INS) tasks to further refine imperfect ranking results, but it often has low interaction efficiency. The active learning (AL) technique addresses this…
Active LearningInstance SearchRe-RankingCascaded Fast and Slow Models for Efficient Semantic Code Search
The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query. Existing approaches are neither effective nor efficient…
Code SearchRe-RankingRetrievalRocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance…
Data AugmentationInformation RetrievalNatural QuestionsOpen-Domain Question Answering+4Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
Since late 2019, COVID-19 has quickly emerged as the newest biomedical domain, resulting in a surge of new information. As with other emergent domains, the discussion surrounding the topic has been rapidly changing, lead…
DiversityMisinformationOpen-Domain Question AnsweringQuestion Answering+1