This paper introduces a method for contextualizing word vectors using an encoder trained on machine translation (MT) data. The encoder, called CoVe, is derived from an attentional sequence-to-sequence model with a long short-term memory (LSTM) encoder. The encoder is trained on multiple MT datasets and then used to generate context vectors for various natural language processing (NLP) tasks. These context vectors are combined with standard word vectors to improve performance on tasks such as sentiment analysis, question classification, entailment, and question answering. The study shows that using CoVe improves performance over traditional word and character vectors on a wide range of NLP tasks. For fine-grained sentiment analysis and entailment, CoVe achieves state-of-the-art results. The effectiveness of CoVe is attributed to its ability to capture contextual information, which is crucial for understanding the meaning of words in different contexts. The paper also explores the impact of the amount of training data on the performance of CoVe. It finds that larger MT datasets lead to better performance on downstream tasks, suggesting that MT data is a valuable resource for transfer learning in NLP. The results indicate that models using CoVe outperform baselines that use random word vectors, pretrained word vectors, or combinations of word and character vectors. The paper compares CoVe with other methods such as skip-thought vectors and finds that CoVe is more suitable for transfer learning due to its supervised training approach and the use of intermediate outputs. The study highlights the potential of using MT data to improve NLP models by leveraging the contextual information captured by the MT-LSTM encoder. The results demonstrate that CoVe can be effectively used to enhance the performance of various NLP tasks, making it a valuable tool for transfer learning in the field.This paper introduces a method for contextualizing word vectors using an encoder trained on machine translation (MT) data. The encoder, called CoVe, is derived from an attentional sequence-to-sequence model with a long short-term memory (LSTM) encoder. The encoder is trained on multiple MT datasets and then used to generate context vectors for various natural language processing (NLP) tasks. These context vectors are combined with standard word vectors to improve performance on tasks such as sentiment analysis, question classification, entailment, and question answering. The study shows that using CoVe improves performance over traditional word and character vectors on a wide range of NLP tasks. For fine-grained sentiment analysis and entailment, CoVe achieves state-of-the-art results. The effectiveness of CoVe is attributed to its ability to capture contextual information, which is crucial for understanding the meaning of words in different contexts. The paper also explores the impact of the amount of training data on the performance of CoVe. It finds that larger MT datasets lead to better performance on downstream tasks, suggesting that MT data is a valuable resource for transfer learning in NLP. The results indicate that models using CoVe outperform baselines that use random word vectors, pretrained word vectors, or combinations of word and character vectors. The paper compares CoVe with other methods such as skip-thought vectors and finds that CoVe is more suitable for transfer learning due to its supervised training approach and the use of intermediate outputs. The study highlights the potential of using MT data to improve NLP models by leveraging the contextual information captured by the MT-LSTM encoder. The results demonstrate that CoVe can be effectively used to enhance the performance of various NLP tasks, making it a valuable tool for transfer learning in the field.