Text embeddings provide a concise representation of the semantics of sentences and larger spans of text, rather than individual words, capturing a wide range of linguistic features. They have found increasing application to a variety of NLP tasks, including machine translation and natural language inference. While most recent breakthroughs in task performance are being achieved by large scale distributional models, there is a growing disconnection between their knowledge representation and traditional semantics, which hinders efforts to capture such knowledge in human interpretable form or explain model inference behaviour. In this tutorial, we examine from basics to the cutting edge research on the analysis and control of text representations, aiming to shorten the gap between deep latent semantics and formal symbolics. This includes the considerations on knowledge formalisation, the linguistic information that can be extracted and measured from distributional models, and intervention techniques that enable explainable reasoning and controllable text generation, covering methods from pooling to LLM-based.

Achieving precise semantic control over the latent spaces of Variational AutoEncoders (VAEs) holds significant value for downstream tasks in NLP as the underlying generative mechanisms could be better localised, explained and improved upon. Recent research, however, has struggled to achieve consistent results, primarily due to the inevitable loss of semantic information in the variational bottleneck and limited control over the decoding mechanism. To overcome these challenges, we investigate discrete latent spaces in Vector Quantized Variational AutoEncoder (VQVAE) to improve semantic control and generation in Transformer-based VAEs. In particular, We propose T5VQVAE, a novel model that leverages the controllability of VQVAE to guide the self-attention mechanism in T5, exploiting its full generalization capabilities. Experimental results indicate that T5VQVAE outperforms existing state-of-the-art VAE models, including Optimus, in terms of control and preservation of semantic information across different tasks such as auto-encoding of sentences and mathematical expressions, text transfer, and inference. Moreover, T5VQVAE exhibits improved reasoning capabilities, suggesting potential applications for downstream natural language and symbolic inference tasks.

Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing. Currently, most disentanglement methods are unsupervised or rely on synthetic datasets with known generative factors. We argue that recurrent syntactic and semantic regularities in textual data can be used to provide the models with both structural biases and generative factors. We leverage the semantic structures present in a representative and semantically dense category of sentence types, definitional sentences, for training a Variational Autoencoder to learn disentangled representations. Our experimental results show that the proposed model outperforms unsupervised baselines on several qualitative and quantitative benchmarks for disentanglement, and it also improves the results in the downstream task of definition modeling.