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Found 7 papers, 3 papers with code
Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders
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.
LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces
Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-level latent spaces.
Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders
The injection of syntactic information in Variational AutoEncoders (VAEs) has been shown to result in an overall improvement of performances and generalisation.
Towards Controllable Natural Language Inference through Lexical Inference Types
They employ the T5 model to directly generate the tree, which can explain how the answer is inferred.
Can Transformers Reason in Fragments of Natural Language?
State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts.
Quasi-symbolic explanatory NLI via disentanglement: A geometrical examination
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control, and understanding downstream task performance in Natural Language Processing.
Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards
An emerging line of research in Explainable NLP is the creation of datasets enriched with human-annotated explanations and rationales, used to build and evaluate models with step-wise inference and explanation generation capabilities.
