1 citations · 1 across the 9 of their papers we have counts for
11 papers
Towards Controllable Natural Language Inference through Lexical Inference Types
Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann +1
Explainable natural language inference aims to provide a mechanism to produce explanatory (abductive) inference chains which ground claims to their supporting premises. A recent co…
Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions
Marco Valentino, Danilo S. Carvalho, André Freitas
Natural language definitions possess a recursive, self-explanatory semantic structure that can support representation learning methods able to preserve explicit conceptual relation…
Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks
Yingji Zhang, Danilo S. Carvalho, André Freitas
Disentangled latent spaces usually have better semantic separability and geometrical properties, which leads to better interpretability and more controllable data generation. While…
Analysis of business process automation as linear time-invariant system network
Mauricio Jacobo-Romero, Danilo S. Carvalho, Andre Freitas
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. We analysed BP productiv…
Montague semantics and modifier consistency measurement in neural language models
Danilo S. Carvalho, Edoardo Manino, Julia Rozanova +2
This work proposes a novel methodology for measuring compositional behavior in contemporary language embedding models. Specifically, we focus on adjectival modifier phenomena in ad…
Estimating productivity gains in digital automation
Mauricio Jacobo-Romero, Danilo S. Carvalho, André Freitas
This paper proposes a novel productivity estimation model to evaluate the effects of adopting Artificial Intelligence (AI) components in a production chain. Our model provides evid…
Quasi-symbolic Semantic Geometry over Transformer-based Variational AutoEncoder
Yingji Zhang, Danilo S. Carvalho, André Freitas
Formal/symbolic semantics can provide canonical, rigid controllability and interpretability to sentence representations due to their \textit{localisation} or \textit{composition} p…
Learning Disentangled Representations for Natural Language Definitions
Danilo S. Carvalho, Giangiacomo Mercatali, Yingji Zhang +1
Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing…
Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective
Edoardo Manino, Julia Rozanova, Danilo Carvalho +2
Metamorphic testing has recently been used to check the safety of neural NLP models. Its main advantage is that it does not rely on a ground truth to generate test cases. However,…
Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities
Danilo S. Carvalho, Duc-Vu Tran, Van-Khanh Tran +1
Legal professionals worldwide are currently trying to get up-to-pace with the explosive growth in legal document availability through digital means. This drives a need for high eff…
Lexical-Morphological Modeling for Legal Text Analysis
Danilo S. Carvalho, Minh-Tien Nguyen, Tran Xuan Chien +1
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…