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Daniel Cer



Natural Language Processing (NLP), Semantics, Representation Learning, Transfer Learning, Multilinguality, Machine Translation (MT)


Daniel Cer is a senior research scientist at Google Research. His work focuses on representation learning using deep learning methods for natural language processing (NLP) tasks such as semantic similarity, question answering (QA), semantic retrieval (SR), bi-text mining and text classification. Prior to Google, he was a post-doc in the Stanford NLP group where he worked on machine translation (MT), semantic textual similarity (STS) and early bilingual embedding models under the guidance of Dan Jurafsky and Chris Manning.

Last updated:

September 22, 2021