Papers › Learned in Translation: Contextualized Word Vectors
Learned in Translation: Contextualized Word Vectors
Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher
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.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | SNLI | Biattentive Classification Network + CoVe + Char | % Test Accuracy | 88.1 | #38 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | Biattentive Classification Network + CoVe + Char | % Train Accuracy | 88.5 | #38 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | Biattentive Classification Network + CoVe + Char | Parameters | 22m | #38 of 98 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | DCN + Char + CoVe | EM | 71.3 | #152 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | DCN + Char + CoVe | F1 | 79.9 | #152 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | DCN (Char + CoVe) | EM | 71.3 | #34 of 55 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | DCN (Char + CoVe) | F1 | 79.9 | #34 of 55 | Archive leaderboard | report |
| Sentiment Analysis | IMDb | BCN+Char+CoVe | Accuracy | 91.8 | #34 of 49 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | BCN+Char+CoVe | Accuracy | 90.3 | #61 of 87 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | BCN+Char+CoVe | Accuracy | 53.7 | #10 of 31 | Archive leaderboard | report |
| Text Classification | TREC-6 | CoVe | Error | 4.2 | #9 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: CoVe
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