Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Neural Re-ranking in Multi-stage Recommender Systems: A Review
As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the adva…
Recommendation SystemsRe-RankingMeasuring and Reducing Model Update Regression in Structured Prediction for NLP
Recent advance in deep learning has led to the rapid adoption of machine learning-based NLP models in a wide range of applications. Despite the continuous gain in accuracy, backward compatibility is also an important asp…
Dependency ParsingKnowledge DistillationPredictionregression+3RescoreBERT: Discriminative Speech Recognition Rescoring with BERT
Second-pass rescoring is an important component in automatic speech recognition (ASR) systems that is used to improve the outputs from a first-pass decoder by implementing a lattice rescoring or $n$-best re-ranking. Whil…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)DecoderLanguage Modelling+4Describing Differences between Text Distributions with Natural Language
How do two distributions of texts differ? Humans are slow at answering this, since discovering patterns might require tediously reading through hundreds of samples. We propose to automatically summarize the differences b…
Binary ClassificationRe-RankingPPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding
Large language models (LM) based on Transformers allow to generate plausible long texts. In this paper, we explore how this generation can be further controlled at decoding time to satisfy certain constraints (eg. being …
Language ModellingRe-RankingEntity Linking via Explicit Mention-Mention Coreference Modeling
Learning representations of entity mentions is a core component of modern entity linking systems for both candidate generation and making linking predictions. In this paper, we present and empirically analyze a novel tra…
Entity LinkingRe-RankingLearning Sense Embeddings from Definitions in Dictionaries
We introduce a method for learning to embed word senses as defined in a given set of given dictionaries. In our approach, sense definition pairs, <word, definition> are transformed into low-dimension vectors aimed at max…
Re-RankingMachine Learning for Food Review and Recommendation
Food reviews and recommendations have always been important for online food service websites. However, reviewing and recommending food is not simple as it is likely to be overwhelmed by disparate contexts and meanings. I…
BIG-bench Machine LearningDeep LearningDependency ParsingPOS+5Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation
Re-ranking models refine item recommendation lists generated by the prior global ranking model, which have demonstrated their effectiveness in improving the recommendation quality. However, most existing re-ranking solut…
Re-RankingEstablishing Strong Baselines for TripClick Health Retrieval
We present strong Transformer-based re-ranking and dense retrieval baselines for the recently released TripClick health ad-hoc retrieval collection. We improve the - originally too noisy - training data with a simple neg…
Re-RankingRetrievalOn Cross-Lingual Retrieval with Multilingual Text Encoders
In this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a number of diverse language pa…
Cross-Lingual TransferRe-RankingRetrievalSentence+2Isometric MT: Neural Machine Translation for Automatic Dubbing
Automatic dubbing (AD) is among the machine translation (MT) use cases where translations should match a given length to allow for synchronicity between source and target speech. For neural MT, generating translations of…
Machine TranslationRe-RankingSelf-LearningTranslationConversational Search with Mixed-Initiative -- Asking Good Clarification Questions backed-up by Passage Retrieval
We deal with the scenario of conversational search, where user queries are under-specified or ambiguous. This calls for a mixed-initiative setup. User-asks (queries) and system-answers, as well as system-asks (clarificat…
Conversational SearchPassage RetrievalRe-RankingRetrievalRe-ranking With Constraints on Diversified Exposures for Homepage Recommender System
The homepage recommendation on most E-commerce applications places items in a hierarchical manner, where different channels display items in different styles. Existing algorithms usually optimize the performance of a sin…
DiversityRecommendation SystemsRe-RankingDeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification
Face identification (FI) is ubiquitous and drives many high-stake decisions made by law enforcement. State-of-the-art FI approaches compare two images by taking the cosine similarity between their image embeddings. Yet, …
Face IdentificationRe-RankingRe-ranking for image retrieval and transductive few-shot classification
In the problems of image retrieval and few-shot classification, the mainstream approaches focus on learning a better feature representation. However, directly tackling the distance or similarity measure between images co…
ClassificationFew-Shot LearningImage RetrievalRe-Ranking+1Ranking Distance Calibration for Cross-Domain Few-Shot Learning
Recent progress in few-shot learning promotes a more realistic cross-domain setting, where the source and target datasets are from different domains. Due to the domain gap and disjoint label spaces between source and tar…
Cross-Domain Few-Shotcross-domain few-shot learningFew-Shot LearningImage Retrieval+2Pre-training Methods in Information Retrieval
The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need. In recent years, the resurgence of deep lear…
Information RetrievalRe-RankingRetrievalGroup based Personalized Search by Integrating Search Behaviour and Friend Network
The key to personalized search is to build the user profile based on historical behaviour. To deal with the users who lack historical data, group based personalized models were proposed to incorporate the profiles of sim…
Re-RankingQuality and Cost Trade-offs in Passage Re-ranking Task
Deep learning models named transformers achieved state-of-the-art results in a vast majority of NLP tasks at the cost of increased computational complexity and high memory consumption. Using the transformer model in real…
Information RetrievalOpen-Domain Question AnsweringPassage Re-RankingRe-Ranking+1