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Found 13 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.
Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions
Natural language definitions possess a recursive, self-explanatory semantic structure that can support representation learning methods able to preserve explicit conceptual relations and constraints in the latent space.
Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks
Disentangled latent spaces usually have better semantic separability and geometrical properties, which leads to better interpretability and more controllable data generation.
Analysis of business process automation as linear time-invariant system network
In this work, we examined Business Process (BP) production as a signal; this novel approach explores a BP workflow as a linear time-invariant (LTI) system.
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.
Montague semantics and modifier consistency measurement in neural language models
At the same time, the need for interpretability has elicited questions on their intrinsic properties and capabilities.
Estimating productivity gains in digital automation
This paper proposes a novel productivity estimation model to evaluate the effects of adopting Artificial Intelligence (AI) components in a production chain.
Learning Disentangled Representations for Natural Language Definitions
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing.
Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities
In this work, a two-stage method for Legal Information Retrieval is proposed, combining lexical statistics and distributional sentence representations in the context of Competition on Legal Information Extraction/Entailment (COLIEE).
Lexical-Morphological Modeling for Legal Text Analysis
In the context of the Competition on Legal Information Extraction/Entailment (COLIEE), we propose a method comprising the necessary steps for finding relevant documents to a legal question and deciding on textual entailment evidence to provide a correct answer.
