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We introduce sub-sentence encoder, a contrastively-learned contextual embedding model for fine-grained semantic representation of text.
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Seeing things from a different angle:discovering diverse perspectives about claims
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PyTorch Lightning
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
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Real-time open-domain question answering with dense-sparse phrase index
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Supervised Contrastive Learning
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Learning to decompose: Hypothetical question decomposition based on comparable texts
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PropSegmEnt: A large-scale corpus for proposition-level segmentation and entailment recognition
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CSTS: Conditional Semantic Textual Similarity
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Decontextualization: Making sentences stand-alone
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SimCSE: Simple contrastive learning of sentence embeddings
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Phrase retrieval learns passage retrieval, too
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Smart: Sentences as basic units for text evaluation
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Attributed question answering: Evaluation and modeling for attributed large language models
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