This paper introduces a variational autoencoder (VAE) for generating sentences, which incorporates distributed latent representations of entire sentences. Unlike traditional recurrent neural network language models (RNNLMs), which generate sentences one word at a time and do not model global sentence properties, the VAE explicitly captures holistic features such as style, topic, and syntax through a continuous latent variable. This allows the model to generate diverse, coherent sentences through deterministic decoding and to interpolate between known sentences by exploring paths in the latent space. The VAE is trained using a variational lower bound objective that balances the reconstruction of input data with the KL divergence between the posterior and prior distributions. To address challenges in training, the paper proposes techniques such as KL cost annealing and word dropout, which help the model learn to encode meaningful global features. The VAE outperforms RNNLMs in language modeling tasks, particularly in imputing missing words, and produces more diverse and plausible sentences than simpler models. The model's latent space is analyzed to show that it captures rich global features, including topic and syntactic structure, and can generate coherent sentences through continuous sampling. The paper also demonstrates that the VAE can generate sentences that are difficult to distinguish from true sentences, as shown through adversarial evaluation. Additionally, the model's ability to interpolate between sentences and generate grammatically correct outputs is highlighted, indicating that the latent space is smooth and well-structured. The VAE is evaluated on tasks such as paraphrase detection and question classification, where it performs competitively with other models. The paper concludes that the VAE provides a powerful framework for generating and understanding natural language, with potential applications in tasks requiring global sentence representations. Future work includes exploring the factorization of latent variables into style and content components and extending the model to fully adversarial training objectives.This paper introduces a variational autoencoder (VAE) for generating sentences, which incorporates distributed latent representations of entire sentences. Unlike traditional recurrent neural network language models (RNNLMs), which generate sentences one word at a time and do not model global sentence properties, the VAE explicitly captures holistic features such as style, topic, and syntax through a continuous latent variable. This allows the model to generate diverse, coherent sentences through deterministic decoding and to interpolate between known sentences by exploring paths in the latent space. The VAE is trained using a variational lower bound objective that balances the reconstruction of input data with the KL divergence between the posterior and prior distributions. To address challenges in training, the paper proposes techniques such as KL cost annealing and word dropout, which help the model learn to encode meaningful global features. The VAE outperforms RNNLMs in language modeling tasks, particularly in imputing missing words, and produces more diverse and plausible sentences than simpler models. The model's latent space is analyzed to show that it captures rich global features, including topic and syntactic structure, and can generate coherent sentences through continuous sampling. The paper also demonstrates that the VAE can generate sentences that are difficult to distinguish from true sentences, as shown through adversarial evaluation. Additionally, the model's ability to interpolate between sentences and generate grammatically correct outputs is highlighted, indicating that the latent space is smooth and well-structured. The VAE is evaluated on tasks such as paraphrase detection and question classification, where it performs competitively with other models. The paper concludes that the VAE provides a powerful framework for generating and understanding natural language, with potential applications in tasks requiring global sentence representations. Future work includes exploring the factorization of latent variables into style and content components and extending the model to fully adversarial training objectives.