no code implementations • 7 Mar 2017 • Bernhard Bermeitinger, André Freitas, Simon Donig, Siegfried Handschuh
This short paper outlines research results on object classification in images of Neoclassical furniture.
no code implementations • COLING 2016 • Christina Niklaus, Bernhard Bermeitinger, Siegfried Handschuh, André Freitas
In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems.
no code implementations • COLING 2018 • Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh
We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction.
no code implementations • WS 2016 • Vivian S. Silva, Manuela Hürliman, Brian Davis, Siegfried Handschuh, André Freitas
This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora.
no code implementations • LREC 2018 • Vivian S. Silva, André Freitas, Siegfried Handschuh
Adopting a conceptual model composed of a set of semantic roles for dictionary definitions, we trained a classifier for automatically labeling definitions, preparing the data to be later converted to a graph representation.
no code implementations • 20 Jun 2018 • Vivian S. Silva, André Freitas, Siegfried Handschuh
Semantic annotation is fundamental to deal with large-scale lexical information, mapping the information to an enumerable set of categories over which rules and algorithms can be applied, and foundational ontology classes can be used as a formal set of categories for such tasks.
no code implementations • WS 2016 • Vivian S. Silva, Siegfried Handschuh, André Freitas
Understanding the semantic relationships between terms is a fundamental task in natural language processing applications.
1 code implementation • COLING 2018 • Matthias Cetto, Christina Niklaus, André Freitas, Siegfried Handschuh
We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification.
1 code implementation • COLING 2018 • Matthias Cetto, Christina Niklaus, André Freitas, Siegfried Handschuh
In that way, we preserve the context of the relational tuples extracted from a source sentence, generating a novel lightweight semantic representation for Open IE that enhances the expressiveness of the extracted propositions.
no code implementations • 28 Dec 2018 • Guy Marshall, André Freitas
Currently, there is no consistent model for visually or formally representing the architecture of AI systems.
no code implementations • 9 Jul 2019 • Vivian S. Silva, André Freitas, Siegfried Handschuh
Artificial Intelligence models are becoming increasingly more powerful and accurate, supporting or even replacing humans' decision making.
1 code implementation • IJCNLP 2019 • Armins Stepanjans, André Freitas
Identifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task.
Ranked #5 on Relation Extraction on SemEval 2018 Task 10
1 code implementation • LREC 2020 • Viktor Schlegel, Marco Valentino, André Freitas, Goran Nenadic, Riza Batista-Navarro
Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text.
1 code implementation • EACL 2021 • Marco Valentino, Mokanarangan Thayaparan, André Freitas
This paper presents a novel framework for reconstructing multi-hop explanations in science Question Answering (QA).
no code implementations • 26 Aug 2020 • Guy Clarke Marshall, Caroline Jay, André Freitas
We utilise Richards-Engelhardt framework as a tool for understanding Natural Language Processing systems diagrams.
no code implementations • 28 Aug 2020 • Guy Clarke Marshall, André Freitas, Caroline Jay
Neural networks are a prevalent and effective machine learning component, and their application is leading to significant scientific progress in many domains.
no code implementations • 25 Sep 2020 • Vivian S. Silva, André Freitas, Siegfried Handschuh
Text entailment, the task of determining whether a piece of text logically follows from another piece of text, is a key component in NLP, providing input for many semantic applications such as question answering, text summarization, information extraction, and machine translation, among others.
no code implementations • COLING 2022 • Marco Valentino, Mokanarangan Thayaparan, André Freitas
Most of the contemporary approaches for multi-hop Natural Language Inference (NLI) construct explanations considering each test case in isolation.
no code implementations • 1 Oct 2020 • Mokanarangan Thayaparan, Marco Valentino, André Freitas
This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC).
no code implementations • 25 Oct 2020 • Mokanarangan Thayaparan, Marco Valentino, André Freitas
We propose a novel approach for answering and explaining multiple-choice science questions by reasoning on grounding and abstract inference chains.
no code implementations • ICLR Workshop Rethinking_ML_Papers 2021 • Guy Clarke Marshall, Caroline Jay, André Freitas
This paper advocates for diagrammatic summary publications for machine learning system architecture papers.
1 code implementation • 24 Mar 2021 • Jordan Meadows, André Freitas
Automating the derivation of published results is a challenge, in part due to the informal use of mathematics by physicists, compared to that of mathematicians.
1 code implementation • ACL 2021 • Deborah Ferreira, Julia Rozanova, Mokanarangan Thayaparan, Marco Valentino, André Freitas
Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models.
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.
no code implementations • 7 May 2021 • Mokanarangan Thayaparan, Marco Valentino, Deborah Ferreira, Julia Rozanova, André Freitas
This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differentiable convex optimization.
no code implementations • ACL (NALOMA, IWCS) 2021 • Julia Rozanova, Deborah Ferreira, Mokanarangan Thayaparan, Marco Valentino, André Freitas
Natural language contexts display logical regularities with respect to substitutions of related concepts: these are captured in a functional order-theoretic property called monotonicity.
1 code implementation • 24 May 2021 • Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh
We present a context-preserving text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences.
1 code implementation • 25 Jul 2021 • Marco Valentino, Mokanarangan Thayaparan, Deborah Ferreira, André Freitas
Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and explainable inference.
no code implementations • Findings (EMNLP) 2021 • Giangiacomo Mercatali, André Freitas
The ability of learning disentangled representations represents a major step for interpretable NLP systems as it allows latent linguistic features to be controlled.
no code implementations • 4 Feb 2022 • Oskar Wysocki, Zili Zhou, Paul O'Regan, Deborah Ferreira, Magdalena Wysocka, Dónal Landers, André Freitas
Specialised transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora.
1 code implementation • 11 Apr 2022 • Oskar Wysocki, Jessica Katharine Davies, Markel Vigo, Anne Caroline Armstrong, Dónal Landers, Rebecca Lee, André Freitas
This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support.
no code implementations • 3 May 2022 • Marco Valentino, André Freitas
A fundamental research goal for Explainable AI (XAI) is to build models that are capable of reasoning through the generation of natural language explanations.
no code implementations • 2 Jul 2022 • Magdalena Wysocka, Oskar Wysocki, Marie Zufferey, Dónal Landers, André Freitas
We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e. g. pathways or Protein-Protein-Interaction networks) and interpretability.
no code implementations • 2 Aug 2022 • Alex Bogatu, Zili Zhou, Dónal Landers, André Freitas
Entailment trees have been proposed to simulate the human reasoning process of explanation generation in the context of open--domain textual question answering.
no code implementations • 5 Aug 2022 • Mokanarangan Thayaparan, Marco Valentino, André Freitas
Integer Linear Programming (ILP) provides a viable mechanism to encode explicit and controllable assumptions about explainable multi-hop inference with natural language.
no code implementations • 3 Oct 2022 • 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.
no code implementations • 10 Oct 2022 • Danilo S. Carvalho, Edoardo Manino, Julia Rozanova, Lucas Cordeiro, André Freitas
At the same time, the need for interpretability has elicited questions on their intrinsic properties and capabilities.
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 • 2 May 2023 • 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.
no code implementations • 4 May 2023 • Maël Jullien, Marco Valentino, Hannah Frost, Paul O'Regan, Donal Landers, André Freitas
This paper describes the results of SemEval 2023 task 7 -- Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT) -- consisting of 2 tasks, a Natural Language Inference (NLI) task, and an evidence selection task on clinical trial data.
2 code implementations • 5 May 2023 • Maël Jullien, Marco Valentino, Hannah Frost, Paul O'Regan, Donal Landers, André Freitas
In this work, we present a novel resource to advance research on NLI for reasoning on CTRs.
1 code implementation • 12 May 2023 • 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 relations and constraints in the latent space.
no code implementations • 1 Aug 2023 • Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh
In that way, we generate a semantic hierarchy of minimal propositions that leads to a novel representation of complex assertions that puts a semantic layer on top of the simplified sentences.
1 code implementation • 2 Nov 2023 • Marco Valentino, Jordan Meadows, Lan Zhang, André Freitas
To this end, we introduce different multi-operational representation paradigms, modelling mathematical operations as explicit geometric transformations.
2 code implementations • 10 Nov 2023 • Maxime Delmas, Magdalena Wysocka, André Freitas
In addition to their evaluation in few-shot settings, we explore the potential of open Large Language Models (Vicuna-13B) as synthetic data generator and propose a new workflow for this purpose.
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 • 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 • 1 Feb 2024 • Xin Quan, Marco Valentino, Louise A. Dennis, André Freitas
An increasing amount of research in Natural Language Inference (NLI) focuses on the application and evaluation of Large Language Models (LLMs) and their reasoning capabilities.
no code implementations • 16 Feb 2024 • Dhairya Dalal, Marco Valentino, André Freitas, Paul Buitelaar
While Large Language Models (LLMs) have found success in real-world applications, their underlying explanatory process is still poorly understood.
no code implementations • 3 Apr 2024 • Julia Rozanova, Marco Valentino, André Freitas
Rigorous evaluation of the causal effects of semantic features on language model predictions can be hard to achieve for natural language reasoning problems.
no code implementations • 3 Apr 2024 • Mokanarangan Thayaparan, Marco Valentino, André Freitas
Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI).
no code implementations • 7 Apr 2024 • Mael Jullien, Marco Valentino, André Freitas
Addressing this, we present SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for ClinicalTrials.
no code implementations • 2 May 2024 • Xin Quan, Marco Valentino, Louise A. Dennis, André Freitas
Natural language explanations have become a proxy for evaluating explainable and multi-step Natural Language Inference (NLI) models.
1 code implementation • COLING (TextGraphs) 2022 • Marco Valentino, Deborah Ferreira, Mokanarangan Thayaparan, André Freitas, Dmitry Ustalov
In this summary paper, we present the results of the 1st edition of the NLPS task, providing a description of the evaluation data, and the participating systems.
no code implementations • COLING (CODI, CRAC) 2022 • Christina Niklaus, André Freitas, Siegfried Handschuh
We present a discourse-aware text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences.
no code implementations • LREC 2022 • Minh-Quoc Nghiem, Paul Baylis, André Freitas, Sophia Ananiadou
We present a case study on the application of text classification and legal judgment prediction for flight compensation.