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Reranking

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Showing 376–400 of 586 papers

TitleStatusHype
Embedding-based Recommender System for Job to Candidate Matching on Scale—0
Modeling Diagnostic Label Correlation for Automatic ICD CodingCode0
Assessing the Use of Prosody in Constituency Parsing of Imperfect TranscriptsCode0
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition—0
RealTranS: End-to-End Simultaneous Speech Translation with Convolutional Weighted-Shrinking Transformer—0
Improving Human Text Simplification with Sentence Fusion—0
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering—0
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems—0
damo_nlp at MEDIQA 2021: Knowledge-based Preprocessing and Coverage-oriented Reranking for Medical Question Summarization—0
Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT—0
Better than BERT but Worse than Baseline—0
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval ModelsCode0
Leveraging Task Information in Grammatical Error Correction for Short Answer Assessment through Context-based Reranking—0
Reranking Machine Translation Hypotheses with Structured and Web-based Language Models—0
Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence—0
First the worst: Finding better gender translations during beam searchCode0
Innovative Bert-based Reranking Language Models for Speech Recognition—0
Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations—0
GRN: Generative Rerank Network for Context-wise Recommendation—0
Query Generation for Multimodal Documents—0
CEQE: Contextualized Embeddings for Query Expansion—0
Multilingual Answer Sentence Reranking via Automatically Translated Data—0
Model Agnostic Answer Reranking System for Adversarial Question Answering—0
Incremental Beam Manipulation for Natural Language GenerationCode0
Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval—0
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