Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation
Citation recommendation systems for the scientific literature, to help authors find papers that should be cited, have the potential to speed up discoveries and uncover new routes for scientific exploration. We treat this…
Citation RecommendationDomain AdaptationRecommendation SystemsRe-Ranking+1Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification
Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predicted flow fields for frame interpolation…
Image Quality AssessmentOptical Flow EstimationRe-RankingAANet: Attribute Attention Network for Person Re-Identifications
This paper proposes Attribute Attention Network (AANet), a new architecture that integrates person attributes and attribute attention maps into a classification framework to solve the person re-identification (re-ID) pro…
AttributePerson Re-IdentificationRe-RankingNeural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results
In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results. We present the content-focused Neural-IR-Explorer, which empowers users to browse thr…
Information RetrievalRe-RankingRetrievalTU Wien @ TREC Deep Learning '19 -- Simple Contextualization for Re-ranking
The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpretable at the same time. However, in most i…
Deep LearningDocument RankingPassage RankingRe-Ranking+1Global Greedy Dependency Parsing
Most syntactic dependency parsing models may fall into one of two categories: transition- and graph-based models. The former models enjoy high inference efficiency with linear time complexity, but they rely on the stacki…
ARCDependency ParsingRe-RankingSentenceLong-span language modeling for speech recognition
We explore neural language modeling for speech recognition where the context spans multiple sentences. Rather than encode history beyond the current sentence using a cache of words or document-level features, we focus ou…
Language ModelingLanguage ModellingRe-RankingSentence+2BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes
This paper presents our participation to the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (…
Medical Concept NormalizationRelation ExtractionRe-RankingWord EmbeddingsEntity Summarization: State of the Art and Future Challenges
The increasing availability of semantic data has substantially enhanced Web applications. Semantic data such as RDF data is commonly represented as entity-property-value triples. The magnitude of semantic data, in partic…
Combinatorial OptimizationInformation RetrievalInformativenessRe-RankingFacebook AI's WAT19 Myanmar-English Translation Task Submission
This paper describes Facebook AI's submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generaliza…
Re-RankingTranslationBioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019. Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health dis…
Re-RankingRetrievalSentenceTask 2Towards a Metric for Automated Conversational Dialogue System Evaluation and Improvement
We present "AutoJudge", an automated evaluation method for conversational dialogue systems. The method works by first generating dialogues based on self-talk, i.e. dialogue systems talking to itself. Then, it uses human …
Open-Ended Question AnsweringReinforcement LearningReinforcement Learning (RL)Re-RankingDeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases
The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set. U…
Learning Word EmbeddingsMulti-Label LearningRe-RankingTAG+1Learning a Fixed-Length Fingerprint Representation
We present DeepPrint, a deep network, which learns to extract fixed-length fingerprint representations of only 200 bytes. DeepPrint incorporates fingerprint domain knowledge, including alignment and minutiae detection, i…
Graph MatchingRe-RankingASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking
In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards gener…
Learning-To-RankRe-RankingCandidate Generation with Binary Codes for Large-Scale Top-N Recommendation
Generating the Top-N recommendations from a large corpus is computationally expensive to perform at scale. Candidate generation and re-ranking based approaches are often adopted in industrial settings to alleviate effici…
Recommendation SystemsRe-RankingLeverage Implicit Feedback for Context-aware Product Search
Product search serves as an important entry point for online shopping. In contrast to web search, the retrieved results in product search not only need to be relevant but also should satisfy customers' preferences in ord…
Re-RankingIncremental Improvement of a Question Answering System by Re-ranking Answer Candidates using Machine Learning
We implement a method for re-ranking top-10 results of a state-of-the-art question answering (QA) system. The goal of our re-ranking approach is to improve the answer selection given the user question and the top-10 cand…
Answer SelectionBIG-bench Machine LearningQuestion AnsweringRe-Ranking+2Global-Local Temporal Representations For Video Person Re-Identification
This paper proposes the Global-Local Temporal Representation (GLTR) to exploit the multi-scale temporal cues in video sequences for video person Re-Identification (ReID). GLTR is constructed by first modeling the short-t…
Metric LearningPerson Re-IdentificationRe-RankingVideo-Based Person Re-IdentificationTwo-Stage Session-based Recommendations with Candidate Rank Embeddings
Recent advances in Session-based recommender systems have gained attention due to their potential of providing real-time personalized recommendations with high recall, especially when compared to traditional methods like…
Collaborative FilteringRecommendation SystemsRe-RankingSession-Based Recommendations+1