1 code implementation • 1 Feb 2024 • Yingji Zhang, Danilo S. Carvalho, Marco Valentino, Ian Pratt-Hartmann, Andre Freitas
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.
no code implementations • 20 Dec 2023 • Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann, André Freitas
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.
1 code implementation • 14 Nov 2023 • Yingji Zhang, Marco Valentino, Danilo S. Carvalho, Ian Pratt-Hartmann, André Freitas
The injection of syntactic information in Variational AutoEncoders (VAEs) has been shown to result in an overall improvement of performances and generalisation.
no code implementations • 7 Aug 2023 • Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann, Andre Freitas
They employ the T5 model to directly generate the tree, which can explain how the answer is inferred.
1 code implementation • 10 Nov 2022 • Viktor Schlegel, Kamen V. Pavlov, Ian Pratt-Hartmann
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.
no code implementations • 12 Oct 2022 • Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann, André Freitas
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control, and understanding downstream task performance in Natural Language Processing.
no code implementations • IWCS (ACL) 2021 • Marco Valentino, Ian Pratt-Hartmann, André Freitas
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.