Noah Constant

Publications by Year

Research Areas

Topic Modeling, Natural Language Processing Techniques, Multimodal Machine Learning Applications, Speech Recognition and Synthesis, Syntax, Semantics, Linguistic Variation

Most-Cited Works

• → mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer(2021)1,508 cited
• → Universal Sentence Encoder(2018)1,292 cited
• → Universal Sentence Encoder for English(2018)1,245 cited
• → Multilingual Universal Sentence Encoder for Semantic Retrieval(2020)404 cited
• → Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models(2022)248 cited
• → ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models(2022)222 cited
• → SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer(2022)167 cited
• → Learning Semantic Textual Similarity from Conversations(2018)155 cited
• → English rise-fall-rise: a study in the semantics and pragmatics of intonation(2012)124 cited