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TitleStatusHype
Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition—0
LEALLA: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation—0
Learning and Evaluating Sparse Interpretable Sentence Embeddings—0
Learning distributed sentence vectors with bi-directional 3D convolutions—0
Learning Interpretable Models Using Uncertainty Oracles—0
Learning Joint Representations of Videos and Sentences with Web Image Search—0
Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification—0
Legal Case Winning Party Prediction With Domain Specific Auxiliary Models—0
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding—0
Linguistic-Based Mild Cognitive Impairment Detection Using Informative Loss—0
Locality Preserving Sentence Encoding—0
LSTM-based Deep Neural Network With A Focus on Sentence Representation for Sequential Sentence Classification in Medical Scientific Abstracts—0
MappSent: a Textual Mapping Approach for Question-to-Question Similarity—0
Measuring a Texts Fairness Dimensions Using Machine Learning Based on Social Psychological Factors—0
Mechanistic Decomposition of Sentence Representations—0
MED-SE: Medical Entity Definition-based Sentence Embedding—0
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models—0
Meta-Embedding Sentence Representation for Textual Similarity—0
Missed Connections: Lateral Thinking Puzzles for Large Language Models—0
MixUp Training Leads to Reduced Overfitting and Improved Calibration for the Transformer Architecture—0
MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters—0
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models—0
Multilingual Medical Question Answering and Information Retrieval for Rural Health Intelligence Access—0
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications—0
Multilingual Seq2seq Training with Similarity Loss for Cross-Lingual Document Classification—0
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