Poster
Learned in Translation: Contextualized Word Vectors
Bryan McCann · James Bradbury · Caiming Xiong · Richard Socher

Tue Dec 5th 06:30 -- 10:30 PM @ Pacific Ballroom #77 #None

Computer vision has benefited from initializing multiple deep layers with weights pretrained on large supervised training sets like ImageNet. Natural language processing (NLP) typically sees initialization of only the lowest layer of deep models with pretrained word vectors. In this paper, we use a deep LSTM encoder from an attentional sequence-to-sequence model trained for machine translation (MT) to contextualize word vectors. We show that adding these context vectors (CoVe) improves performance over using only unsupervised word and character vectors on a wide variety of common NLP tasks: sentiment analysis (SST, IMDb), question classification (TREC), entailment (SNLI), and question answering (SQuAD). For fine-grained sentiment analysis and entailment, CoVe improves performance of our baseline models to the state of the art.

Author Information

Bryan McCann (Salesforce Research)
James Bradbury (Salesforce Research)
Caiming Xiong (Salesforce)
Richard Socher (MetaMind)

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