Source-linked AI summary
Generating Sentences from a Continuous Space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, Samy Bengio
TL;DR
RNNLMs lack explicit global sentence representations, limiting direct modeling of holistic properties and inference over missing words. The paper introduces an RNN-based variational autoencoder with continuous sentence latents and training techniques for its difficult optimization. It reports effective missing-word imputation, coherent deterministic generation, and smooth sentence interpolations, while finding no advantage over RNNLMs for ordinary language modeling.
Problem
RNNLMs do not expose interpretable global sentence features, and their directional structure makes inference over unknown words difficult.
Method
The paper uses an RNN-based variational autoencoder with a continuous latent representation for each sentence and specialized imputation inference.
Results
The model effectively imputes missing words and generates diverse, coherent sentences with deterministic decoding, while matching existing RNNLM performance on standard language modeling.
Takeaways & Limitations
A continuous latent sentence space supports global information propagation for imputation and smooth, interpretable interpolations between sentences.
Takeaways & Limitations
The model does not improve standard language modeling because training still favors local statistics and struggles to make the latent variable dominate reconstruction.
Abstract
from arXiv · showhide
The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work, we introduce and study an RNN-based variational autoencoder generative model that incorporates distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features. Samples from the prior over these sentence representations remarkably produce diverse and well-formed sentences through simple deterministic decoding. By examining paths through this latent space, we are able to generate coherent novel sentences that interpolate between known sentences. We present techniques for solving the difficult learning problem presented by this model, demonstrate its effectiveness in imputing missing words, explore many interesting properties of the model's latent sentence space, and present negative results on the use of the model in language modeling.
1 Introduction
RNNLMs model sentences through sequential next-word predictions but lack an explicit interpretable representation of global sentence features. The paper introduces a variational autoencoder extension and evaluates its latent representations across language modeling, imputation, and sentence-space analysis.
- RNNLMs generate sentences word by word but do not expose interpretable representations of global features such as topic or high-level syntax.
- The proposed model explicitly captures global sentence features in a continuous latent variable using a variational autoencoder architecture.
- The model achieves similar performance to existing RNNLMs on language modeling, where an explicit global variable is not required.
- For missing-word imputation, the model uses an adversarial classifier evaluation and performs well where simpler models fail.
- Latent-space analysis produces diverse, coherent sentences through deterministic decoding and smooth interpolations between sentences.
2 Background
The background contrasts non-generative sentence encoders with variational autoencoders, which impose a prior that regularizes latent codes and enables sampling from a generative sentence model.
- Standard RNN language models lack vector representations of complete sentences, motivating unsupervised methods for mapping sentences to distributed codes.
- Sequence autoencoders encode token sequences with an RNN and decode them probabilistically from the resulting code.
- Conventional autoencoders produce ungrammatical, sharply changing interpolations and lack a prior for assigning sentence probabilities or sampling novel sentences.
- Skip-thought and paragraph-vector models learn sentence encodings but are not presented as generative models for the target sentence.
- A VAE replaces deterministic encoding with a learned posterior and imposes a prior that regularizes latent geometry, enabling proper sampling.
- The VAE objective keeps posterior codes near a prior while providing a valid lower bound on data log likelihood, making the model generative.
3 A VAE for sentences
The sentence VAE uses recurrent encoder–decoder components with a Gaussian latent code, but training tends to ignore that code. The paper addresses this with KL annealing and word dropout, while reporting limited gains from more complex recognition models.
- The model uses single-layer LSTM encoder and decoder RNNs, with a Gaussian prior regularizing the hidden sentence code.
- More sophisticated recognition models, including DRAW-style sampling and normalizing flows, produced no significant gains over the plain VAE.
- Optimization challenges: Straightforward training often sets q(z|x) equal to the prior, making the KL term zero and preventing the latent variable from carrying useful information.
- Optimization challenges: The decoder can ignore z because local sequential statistics are easier to optimize, leaving little gradient signal between encoder and decoder.
- Optimization challenges: KL cost annealing starts with zero KL weight and gradually increases it to 1, encouraging informative yet prior-compatible encodings.
- Optimization challenges: Word dropout replaces conditioned-on previous tokens with UNK during training, weakening the decoder and forcing greater reliance on the latent variable.
4 Results: Language modeling
On Penn Treebank language modeling, the VAE performs slightly worse than the RNNLM baseline while using its latent space to a limited extent. In an inputless-decoder setting, the latent variable substantially improves performance over a weakened RNNLM, but training remains dominated by reconstruction.
- Standard setting: Without word dropout and cost annealing, the VAE matches the baseline RNNLM and uses zero KL divergence.These training techniques are therefore necessary for the model to encode a non-trivial amount in its latent variable in the standard setting.
- Standard setting: The VAE performs slightly worse than the RNNLM baseline, with reconstruction cost 99 offset by a KL divergence cost of 2.The reported VAE value is a variational lower bound, whereas the RNNLM baseline uses exact test likelihood, potentially disadvantaging the VAE.
- Inputless decoder: With an inputless decoder, the VAE shows a substantially improved variational lower bound over a weakened RNNLM limited essentially to unigram statistics.This setting uses a word-dropout keep rate of 0 and produces a fully differentiable sentence-generation process.
- Evaluation: Table 2 reports Penn Treebank negative log likelihood and perplexity, with lower values better and the VAE KL term shown alongside total likelihood.The table compares these metrics across language-modeling systems.
- Training limitation: Even with the proposed techniques and an inputless decoder, the KL divergence term does not dominate reconstruction during training.The result suggests that local statistics remain easier to learn than information encoded in the global latent variable.
5 Results: Imputing missing words
The VAE is designed to impute missing words by using global sentence features, and its imputations are more diverse, plausible, and difficult to distinguish from true endings than RNNLM completions.
- The VAE targets missing-word imputation by using global sentence features to propagate information across known and unknown words.Its recognition model and global latent variable make information flow between variables more easily than in the directional RNNLM.
- The VAE produced more diverse and plausible imputations, while RNNLM completions were more stereotypical and favored generic high-probability endings.The VAE substantially outperformed the baseline under adversarial evaluation by propagating information bidirectionally through its latent variable.
- Models were trained on the Books Corpus, using approximately 80m sentences, 75% word dropout, right-to-left decoding, and 512 hidden units.
- Imputation used beam search with beam size 15 for the RNNLM and three approximate iterated-conditional-modes steps with beam size 5 for the VAE.Both models imputed the final 20% of each sentence under matched computational conditions.
- A novel adversarial evaluation trained unigram and LSTM classifiers to distinguish complete sentences from sentences with imputed completions.Adversarial error was defined as the gap between ideal 50% discriminator accuracy and achieved accuracy.
- The RNNLM’s beam-search setup restricted its final token to the top 15 tokens from its initial unconditional distribution, reducing sample diversity.
6 Analyzing variational models
The analysis tests how the latent sentence representation captures variation through deterministic generation, posterior decoding, and interpolations between sentence codes.
- Sampling from the latent space: Gaussian prior samples decoded greedily can produce diverse sentences, separating variation captured by z from local language-model dependencies.The procedure uses deterministic token selection rather than sampling decoder outputs.
- Word dropout: Lower keep rates increase information stored in z, while overall likelihood degrades somewhat; heavy dropout harms a no-latent-variable model more.The no-dropout model encodes little information in z, reflected by a small KL component.
- Word dropout: Increasing word dropout makes generated sentences more varied, but excessive dropout causes repetition or ungrammaticality.With a fully dropped-out decoder, the model still captures higher-order statistics beyond unigram statistics.
- Rare samples: Rare latent-space samples produce less typical but mostly grammatical sentences that retain clear topics, indicating rich global-feature variation.The samples are obtained by stretching selected eigenspaces while remaining not too improbable under the prior.
- Posterior codes and homotopies: Unlike the variational model, a vanilla sequence autoencoder produces sharp, ungrammatical transitions between sentences along interpolation paths.This comparison supports the view that the variational code space is smoother and more space-filling.
- Posterior codes and homotopies: Posterior codes capture sentence length and parts of speech, while nearby interpolations remain grammatical and usually preserve topic and syntactic structure.Similar syntax and topic can coexist with opposite sentiment valence in nearby embeddings.
7 Conclusion
The paper introduces a variational autoencoder for natural-language sentences and analyzes its latent space. It reports coherent, diverse continuous samples, smooth sentence interpolations, and effective missing-word imputation, while identifying future extensions.
- The paper introduces a variational autoencoder for natural-language sentences and techniques for training it successfully.
- The model effectively imputes missing words and generates coherent, diverse sentences through continuous sampling.
- The learned latent space provides interpretable homotopies that smoothly interpolate between sentences.
- Future work includes factorizing latent variables into style and content, conditioning on extrinsic features, and extending adversarial evaluation to training.
Text classification
The text-classification experiments assess sentence vectors on paraphrase detection and question classification, finding complementary strengths but weaker performance than selected task-specific representations.
- Experimental setup: The experiments extract posterior-mean sentence vectors from a VAE trained on the Books Corpus and evaluate them with classifiers.The protocol follows earlier sentence-representation work.
- Paraphrase detection: On paraphrase detection, VAE features perform slightly worse than skip-thought features but slightly better than recursive autoencoder features.
- Paraphrase detection: Concatenating VAE and skip-thought features yields strong performance, indicating that the representations are complementary.
- Question classification: A linear classifier may disadvantage VAE representations because their Gaussian hidden-code distribution can discourage linear separability.
- Question classification: On question classification, VAE codes do not outperform CBOW but do outperform features from a plain sequence autoencoder.The comparison includes several classifiers and representation baselines.
Hyperparameter tuning
The experiments use extensive Bayesian hyperparameter tuning over development data before reporting language-modeling results.
- Each model undergoes Bayesian hyperparameter tuning for 200 runs, with 12 experiments operated in parallel for 10 hours per setting.The best configuration is selected using development-set performance.
Additional homotopies
The model generates homotopies between latent-space points whose intermediate sentences are almost always grammatical. Nearby interpolations often preserve consistent topic, vocabulary, and syntactic information, with dramatic themes reflecting the fiction training data.
- Intermediate sentences in latent-space homotopies are almost always grammatical.
- Local neighborhoods often preserve consistent topic, vocabulary, and syntactic information as sentences interpolate between endpoints.
- The generated homotopies frequently contain dramatic topics because the model was trained on fiction, including romance novels.