Introduces textual belief states and factorized GRPO to enforce strict latent state mediation in text-based world models, yielding preserved prediction accuracy with large gains in representation quality and rollout performance on TextWorld and ScienceWorld.
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Generating Sentences from a Continuous Space
12 Pith papers cite this work. Polarity classification is still indexing.
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abstract
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.
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T5 casts all NLP tasks as text-to-text generation, systematically explores pre-training choices, and reaches strong performance on summarization, QA, classification and other tasks via large-scale training on the Colossal Clean Crawled Corpus.
VACS is a two-level hierarchical VAE that generates diverse code-switched sentences, and augmenting monolingual data with its output reduces language model perplexity by 33.06%.
Hugging Face releases an open-source Python library that supplies a unified API and pretrained weights for major Transformer architectures used in natural language processing.
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DPText learns text representations that are differentially private, free of private attributes, and retain utility for NLP tasks.
MIDI-Sandwich is a hierarchical VAE-GAN architecture that generates structured 136-beat melodies by modeling local bars and global relationships on the Nottingham dataset.
Extends Social-STGCNN with CVAE for multimodal trajectory prediction and reports moderate gains plus better diversity on ETH/UCY benchmarks and robot data.
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Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation
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A Deep Generative Model for Code-Switched Text
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